Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
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mmdetection | mmdetection-master/configs/res2net/metafile.yml | Models:
- Name: faster_rcnn_r2_101_fpn_2x_coco
In Collection: Faster R-CNN
Config: configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
Metadata:
Training Memory (GB): 7.4
Epochs: 24
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Trai... | 4,908 | 32.394558 | 157 | yml |
mmdetection | mmdetection-master/configs/resnest/README.md | # ResNeSt
> [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
<!-- [BACKBONE] -->
## Abstract
It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attent... | 12,139 | 219.727273 | 757 | md |
mmdetection | mmdetection-master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 261 | 31.75 | 78 | py |
mmdetection | mmdetection-master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
... | 4,255 | 34.764706 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 262 | 31.875 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_... | 4,127 | 34.282051 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 261 | 31.75 | 78 | py |
mmdetection | mmdetection-master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_in... | 1,947 | 29.920635 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 253 | 30.75 | 70 | py |
mmdetection | mmdetection-master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indice... | 2,068 | 30.830769 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/metafile.yml | Models:
- Name: faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco
In Collection: Faster R-CNN
Config: configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
Metadata:
Training Memory (GB): 4.8
Epochs: 12
Training Data: COCO
Training Technique... | 8,748 | 36.874459 | 231 | yml |
mmdetection | mmdetection-master/configs/resnet_strikes_back/README.md | # ResNet strikes back
> [ResNet strikes back: An improved training procedure in timm](https://arxiv.org/abs/2110.00476)
<!-- [OTHERS] -->
## Abstract
The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default... | 5,147 | 124.560976 | 566 | md |
mmdetection | mmdetection-master/configs/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
m... | 612 | 31.263158 | 135 | py |
mmdetection | mmdetection-master/configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model ... | 607 | 31 | 135 | py |
mmdetection | mmdetection-master/configs/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = d... | 604 | 30.842105 | 135 | py |
mmdetection | mmdetection-master/configs/resnet_strikes_back/metafile.yml | Models:
- Name: faster_rcnn_r50_fpn_rsb-pretrain_1x_coco
In Collection: Faster R-CNN
Config: configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
Metadata:
Training Memory (GB): 3.9
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
... | 4,313 | 35.871795 | 203 | yml |
mmdetection | mmdetection-master/configs/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = ... | 605 | 30.894737 | 135 | py |
mmdetection | mmdetection-master/configs/retinanet/README.md | # RetinaNet
> [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002)
<!-- [ALGORITHM] -->
## Abstract
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one... | 13,116 | 241.907407 | 1,226 | md |
mmdetection | mmdetection-master/configs/retinanet/ascend_retinanet_r18_fpn_1x8_1x_coco.py | _base_ = [
'../_base_/models/ascend_retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# data
data = dict(samples_per_gpu=8)
# optimizer
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrai... | 743 | 30 | 79 | py |
mmdetection | mmdetection-master/configs/retinanet/metafile.yml | Collections:
- Name: RetinaNet
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Focal Loss
- FPN
- ResNet
Paper:
URL: https://arxiv.org/abs/1708.02002
... | 10,372 | 32.140575 | 181 | yml |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 222 | 26.875 | 67 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 262 | 31.875 | 57 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 251 | 35 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# data
data = dict(samples_per_gpu=8)
# optimizer
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', c... | 736 | 29.708333 | 79 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r18_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# optimizer
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
... | 627 | 32.052632 | 79 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg... | 1,408 | 32.547619 | 72 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg... | 1,552 | 32.042553 | 72 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 160 | 31.2 | 55 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 160 | 31.2 | 55 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 260 | 31.625 | 72 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 146 | 28.4 | 53 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_90k_coco.py | _base_ = 'retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=10000)
evalu... | 364 | 21.8125 | 69 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
# set grad_norm for stability during mixed-precision training
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 236 | 28.625 | 61 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 174 | 28.166667 | 75 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
optimizer = dict(type='SGD', lr=0.01)
| 272 | 29.333333 | 75 | py |
mmdetection | mmdetection-master/configs/rfnext/README.md | # RF-Next: Efficient Receptive Field Search for CNN
> [RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks](http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf)
<!-- [ALGORITHM] -->
## Abstract
Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. L... | 26,501 | 199.772727 | 1,098 | md |
mmdetection | mmdetection-master/configs/rfnext/metafile.yml | Collections:
- Name: RF-Next
Metadata:
Training Data: COCO
Training Techniques:
- RF-Next
Training Resources: 8x V100 GPUs
Architecture:
- RF-Next
Paper:
URL: http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf
Title: "RF-Next: Efficient Receptive Field Sea... | 9,654 | 37.62 | 324 | yml |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py | _base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step... | 732 | 29.541667 | 106 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
custom_hooks = [
dict(
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json', # noqa
verbose=True,
by_epoch=Tr... | 638 | 28.045455 | 118 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(
type='RFSearchHook',
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_... | 717 | 28.916667 | 116 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
... | 1,348 | 27.702128 | 109 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py | _base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json', # noqa
verbo... | 682 | 28.695652 | 114 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py | _base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_ste... | 698 | 29.391304 | 106 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json', # noqa
ver... | 668 | 28.086957 | 118 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config... | 718 | 28.958333 | 116 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
... | 1,349 | 27.723404 | 109 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py | _base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json', # noqa
verb... | 683 | 28.73913 | 114 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py | _base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
... | 577 | 25.272727 | 106 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
... | 533 | 23.272727 | 62 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
ste... | 585 | 24.478261 | 62 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
... | 1,223 | 25.608696 | 87 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py | _base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,... | 552 | 24.136364 | 65 | py |
mmdetection | mmdetection-master/configs/rpn/README.md | # RPN
> [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
<!-- [ALGORITHM] -->
## Abstract
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduce... | 7,534 | 187.375 | 1,311 | md |
mmdetection | mmdetection-master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py | _base_ = './rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 216 | 26.125 | 67 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r101_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191 | 26.428571 | 61 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r101_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191 | 26.428571 | 61 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_caffe_c4_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# dataset settings
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type=... | 1,352 | 33.692308 | 72 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dic... | 1,407 | 32.52381 | 72 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
... | 776 | 39.894737 | 78 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 141 | 22.666667 | 53 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/sabl/README.md | # SABL
> [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260)
<!-- [ALGORITHM] -->
## Abstract
Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding ... | 9,123 | 189.083333 | 1,361 | md |
mmdetection | mmdetection-master/configs/sabl/metafile.yml | Collections:
- Name: SABL
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- SABL
Paper:
URL: https://arxiv.org/abs/1912.04260
Title: '... | 4,809 | 33.113475 | 170 | yml |
mmdetection | mmdetection-master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint... | 3,296 | 35.230769 | 79 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
... | 3,155 | 35.275862 | 79 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://re... | 1,369 | 34.128205 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
_delete_=True,
type='SABLHead',
num_classes=80,
... | 1,228 | 34.114286 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='t... | 1,760 | 31.018182 | 73 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 1,849 | 31.45614 | 73 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 2,474 | 32.445946 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 2,474 | 32.445946 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_chann... | 1,619 | 30.764706 | 73 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
bbox_head=dict(
_delete_=True,
t... | 1,708 | 31.245283 | 73 | py |
mmdetection | mmdetection-master/configs/scnet/README.md | # SCNet
> [SCNet: Training Inference Sample Consistency for Instance Segmentation](https://arxiv.org/abs/2012.10150)
<!-- [ALGORITHM] -->
## Abstract
<!-- [ABSTRACT] -->
Cascaded architectures have brought significant performance improvement in object detection and instance segmentation. However, there are lingeri... | 5,789 | 89.46875 | 1,098 | md |
mmdetection | mmdetection-master/configs/scnet/metafile.yml | Collections:
- Name: SCNet
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- SCNet
Paper:
URL: https://arxiv.org/abs/2012.10150
Title:... | 3,359 | 27.717949 | 139 | yml |
mmdetection | mmdetection-master/configs/scnet/scnet_r101_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 194 | 26.857143 | 61 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_r50_fpn_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='SCNet',
roi_head=dict(
_delete_=True,
type='SCNetRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=... | 5,020 | 35.649635 | 79 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_r50_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 142 | 27.6 | 53 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 440 | 26.5625 | 76 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py | _base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
data = dict(samples_per_gpu=1, workers_per_gpu=1)
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_... | 355 | 38.555556 | 72 | py |
mmdetection | mmdetection-master/configs/scratch/README.md | # Scratch
> [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883)
<!-- [ALGORITHM] -->
## Abstract
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization. The results are no worse than their ImageNet pre... | 4,105 | 113.055556 | 1,294 | md |
mmdetection | mmdetection-master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_resi... | 816 | 31.68 | 72 | py |
mmdetection | mmdetection-master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residua... | 856 | 31.961538 | 72 | py |
mmdetection | mmdetection-master/configs/scratch/metafile.yml | Collections:
- Name: Rethinking ImageNet Pre-training
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- RPN
- ResNet
Paper:
URL: https://arxiv.org/ab... | 1,707 | 33.857143 | 201 | yml |
mmdetection | mmdetection-master/configs/seesaw_loss/README.md | # Seesaw Loss
> [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032)
<!-- [ALGORITHM] -->
## Abstract
Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. However, they fail to perform as accurately in real-world scenarios, where the category distr... | 10,851 | 225.083333 | 1,365 | md |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | 4,807 | 35.150376 | 79 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 223 | 36.333333 | 94 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvi... | 3,783 | 37.222222 | 79 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 227 | 37 | 98 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 227 | 31.571429 | 71 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 253 | 35.285714 | 97 | py |