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
values |
|---|---|---|---|---|---|---|
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 231 | 32.142857 | 75 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_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')))
| 257 | 35.857143 | 101 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', tem... | 2,510 | 32.039474 | 77 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 200 | 32.5 | 71 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', ... | 1,486 | 34.404762 | 77 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 204 | 33.166667 | 75 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/metafile.yml | Collections:
- Name: Seesaw Loss
Metadata:
Training Data: LVIS
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Softmax
- RPN
- Convolution
- Dense Connections
- FPN
- Re... | 7,809 | 37.284314 | 166 | yml |
mmdetection | mmdetection-master/configs/selfsup_pretrain/README.md | # Backbones Trained by Self-Supervise Algorithms
<!-- [OTHERS] -->
## Abstract
Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large n... | 10,082 | 90.663636 | 1,510 | md |
mmdetection | mmdetection-master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-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'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 418 | 28.928571 | 78 | py |
mmdetection | mmdetection-master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 1,072 | 31.515152 | 78 | py |
mmdetection | mmdetection-master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-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'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 416 | 28.785714 | 76 | py |
mmdetection | mmdetection-master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 1,070 | 31.454545 | 77 | py |
mmdetection | mmdetection-master/configs/simple_copy_paste/README.md | # SimpleCopyPaste
> [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://arxiv.org/abs/2012.07177)
<!-- [ALGORITHM] -->
## Abstract
Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. ... | 6,383 | 162.692308 | 1,136 | md |
mmdetection | mmdetection-master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_270k_coco_instance.py',
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after http... | 813 | 37.761905 | 76 | py |
mmdetection | mmdetection-master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py'
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
lr_config = dict(
warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
runner = dict(type='IterBased... | 346 | 42.375 | 71 | py |
mmdetection | mmdetection-master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_scp_270k_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after h... | 816 | 37.904762 | 76 | py |
mmdetection | mmdetection-master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py'
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
lr_config = dict(
warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
runner = dict(type='IterB... | 350 | 42.875 | 75 | py |
mmdetection | mmdetection-master/configs/simple_copy_paste/metafile.yml | Collections:
- Name: SimpleCopyPaste
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 32x A100 GPUs
Architecture:
- Softmax
- RPN
- Convolution
- Dense Connections
- FPN
... | 3,526 | 36.924731 | 231 | yml |
mmdetection | mmdetection-master/configs/solo/README.md | # SOLO
> [SOLO: Segmenting Objects by Locations](https://arxiv.org/abs/1912.04488)
<!-- [ALGORITHM] -->
## Abstract
We present a new, embarrassingly simple approach to instance segmentation in images. Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances... | 6,475 | 116.745455 | 1,258 | md |
mmdetection | mmdetection-master/configs/solo/decoupled_solo_light_r50_fpn_3x_coco.py | _base_ = './decoupled_solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64... | 2,062 | 31.234375 | 78 | py |
mmdetection | mmdetection-master/configs/solo/decoupled_solo_r50_fpn_1x_coco.py | _base_ = [
'./solo_r50_fpn_1x_coco.py',
]
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOHead',
num_classes=80,
in_channels=256,
stacked_convs=7,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 96), (48, 192), (96, 384),... | 822 | 27.37931 | 78 | py |
mmdetection | mmdetection-master/configs/solo/decoupled_solo_r50_fpn_3x_coco.py | _base_ = './solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOHead',
num_classes=80,
in_channels=256,
stacked_convs=7,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 96), (48, 192), (96, 384), (192, 7... | 775 | 28.846154 | 78 | py |
mmdetection | mmdetection-master/configs/solo/metafile.yml | Collections:
- Name: SOLO
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- Convolution
- ResNet
Paper: https://arxiv.org/abs/1912.04488
README: config... | 3,420 | 28.491379 | 168 | yml |
mmdetection | mmdetection-master/configs/solo/solo_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='SOLO',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 1,523 | 27.222222 | 78 | py |
mmdetection | mmdetection-master/configs/solo/solo_r50_fpn_3x_coco.py | _base_ = './solo_r50_fpn_1x_coco.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'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 800... | 942 | 31.517241 | 77 | py |
mmdetection | mmdetection-master/configs/solov2/README.md | # SOLOv2
> [SOLOv2: Dynamic and Fast Instance Segmentation](https://arxiv.org/abs/2003.10152)
<!-- [ALGORITHM] -->
## Abstract
In this work, we aim at building a simple, direct, and fast instance segmentation
framework with strong performance. We follow the principle of the SOLO method of
Wang et al. "SOLO: segment... | 7,591 | 125.533333 | 478 | md |
mmdetection | mmdetection-master/configs/solov2/metafile.yml | Collections:
- Name: SOLOv2
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x A100 GPUs
Architecture:
- FPN
- Convolution
- ResNet
Paper: https://arxiv.org/abs/2003.10152
README: conf... | 3,914 | 31.625 | 154 | yml |
mmdetection | mmdetection-master/configs/solov2/solov2_light_r18_fpn_3x_coco.py | _base_ = 'solov2_light_r50_fpn_3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=18, init_cfg=dict(checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 213 | 25.75 | 70 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_light_r34_fpn_3x_coco.py | _base_ = 'solov2_light_r50_fpn_3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=34, init_cfg=dict(checkpoint='torchvision://resnet34')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 213 | 25.75 | 70 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_light_r50_dcn_fpn_3x_coco.py | _base_ = 'solov2_r50_fpn_3x_coco.py'
# model settings
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=dict(
feat_channels=256,
stacked_convs=3,
scale_ranges=((1, 64),... | 1,991 | 30.619048 | 78 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_light_r50_fpn_3x_coco.py | _base_ = 'solov2_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
stacked_convs=2,
feat_channels=256,
scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
mask_feature_head=dict(out_channels=128)))
# learning policy
lr_config = dict(
policy='s... | 1,747 | 29.137931 | 78 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_r101_dcn_fpn_3x_coco.py | _base_ = 'solov2_r50_fpn_3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(checkpoint='torchvision://resnet101'),
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=dict(
... | 452 | 31.357143 | 78 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_r101_fpn_3x_coco.py | _base_ = 'solov2_r50_fpn_3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101, init_cfg=dict(checkpoint='torchvision://resnet101')))
| 161 | 22.142857 | 72 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='SOLOv2',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,... | 1,825 | 28.451613 | 78 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_r50_fpn_3x_coco.py | _base_ = 'solov2_r50_fpn_1x_coco.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'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 800... | 942 | 31.517241 | 77 | py |
mmdetection | mmdetection-master/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py | _base_ = 'solov2_r50_fpn_3x_coco.py'
# model settings
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=di... | 555 | 29.888889 | 78 | py |
mmdetection | mmdetection-master/configs/sparse_rcnn/README.md | # Sparse R-CNN
> [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://arxiv.org/abs/2011.12450)
<!-- [ALGORITHM] -->
## Abstract
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as... | 6,964 | 177.589744 | 1,110 | md |
mmdetection | mmdetection-master/configs/sparse_rcnn/metafile.yml | Collections:
- Name: Sparse R-CNN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- Sparse R-CNN
Paper:
URL: https://arxiv.org/abs/2011.1245... | 3,153 | 37.938272 | 229 | yml |
mmdetection | mmdetection-master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py | _base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 235 | 28.5 | 78 | py |
mmdetection | mmdetection-master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py | _base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 216 | 26.125 | 61 | py |
mmdetection | mmdetection-master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='SparseRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 3,469 | 35.145833 | 79 | py |
mmdetection | mmdetection-master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py | _base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], t... | 2,191 | 40.358491 | 78 | py |
mmdetection | mmdetection-master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py | _base_ = './sparse_rcnn_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
... | 853 | 34.583333 | 77 | py |
mmdetection | mmdetection-master/configs/ssd/README.md | # SSD
> [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325)
<!-- [ALGORITHM] -->
## Abstract
We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different a... | 6,646 | 104.507937 | 1,486 | md |
mmdetection | mmdetection-master/configs/ssd/ascend_ssd300_coco.py | _base_ = [
'../_base_/models/ascend_ssd300.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=Tr... | 2,371 | 31.493151 | 79 | py |
mmdetection | mmdetection-master/configs/ssd/metafile.yml | Collections:
- Name: SSD
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- VGG
Paper:
URL: https://arxiv.org/abs/1512.02325
Title: 'SSD: Single Shot MultiBox Detecto... | 2,277 | 27.835443 | 169 | yml |
mmdetection | mmdetection-master/configs/ssd/ssd300_coco.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_p... | 2,360 | 31.791667 | 79 | py |
mmdetection | mmdetection-master/configs/ssd/ssd300_fp16_coco.py | _base_ = ['./ssd300_coco.py']
fp16 = dict(loss_scale='dynamic')
# learning policy
# In order to avoid non-convergence in the early stage of
# mixed-precision training, the warmup in the lr_config is set to linear,
# warmup_iters increases and warmup_ratio decreases.
lr_config = dict(warmup='linear', warmup_iters=1000... | 345 | 33.6 | 75 | py |
mmdetection | mmdetection-master/configs/ssd/ssd512_coco.py | _base_ = 'ssd300_coco.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
... | 2,820 | 32.188235 | 79 | py |
mmdetection | mmdetection-master/configs/ssd/ssd512_fp16_coco.py | _base_ = ['./ssd512_coco.py']
# fp16 settings
fp16 = dict(loss_scale='dynamic')
# learning policy
# In order to avoid non-convergence in the early stage of
# mixed-precision training, the warmup in the lr_config is set to linear,
# warmup_iters increases and warmup_ratio decreases.
lr_config = dict(warmup='linear', wa... | 360 | 35.1 | 75 | py |
mmdetection | mmdetection-master/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='C... | 4,928 | 31.642384 | 77 | py |
mmdetection | mmdetection-master/configs/strong_baselines/README.md | # Strong Baselines
<!-- [OTHERS] -->
We train Mask R-CNN with large-scale jitter and longer schedule as strong baselines.
The modifications follow those in [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/configs/new_baselines).
## Results and Models
| Backbone | Style | Lr schd | Mem (GB) ... | 1,636 | 76.952381 | 182 | md |
mmdetection | mmdetection-master/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
# Requires MMCV-full afte... | 2,703 | 32.382716 | 77 | py |
mmdetection | mmdetection-master/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py | _base_ = 'mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
fp16 = dict(loss_scale=512.)
| 102 | 33.333333 | 72 | py |
mmdetection | mmdetection-master/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py | _base_ = './mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 16 (for 400 epochs)
data = dict(train=dict(times=4 * 4))
lr_config = dict(warmup_iters=500 * 4)
| 261 | 36.428571 | 74 | py |
mmdetection | mmdetection-master/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
# Requires MMCV-full afte... | 893 | 37.869565 | 77 | py |
mmdetection | mmdetection-master/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# use FP16
fp16 = dict(loss_scale=512.)
| 107 | 26 | 66 | py |
mmdetection | mmdetection-master/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
data = dict(train=dict(times=2))
| 208 | 33.833333 | 66 | py |
mmdetection | mmdetection-master/configs/swin/README.md | # Swin
> [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
<!-- [BACKBONE] -->
## Abstract
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Tr... | 5,732 | 135.5 | 1,455 | md |
mmdetection | mmdetection-master/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py | _base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
depths=[2, 2, 18, 2],
init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| 318 | 44.571429 | 124 | py |
mmdetection | mmdetection-master/configs/swin/mask_rcnn_swin-t-p4-w7_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'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type... | 1,301 | 29.27907 | 123 | py |
mmdetection | mmdetection-master/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py | _base_ = './mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py'
# you need to set mode='dynamic' if you are using pytorch<=1.5.0
fp16 = dict(loss_scale=dict(init_scale=512))
| 169 | 41.5 | 64 | py |
mmdetection | mmdetection-master/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
ty... | 3,305 | 34.934783 | 123 | py |
mmdetection | mmdetection-master/configs/swin/metafile.yml | Models:
- Name: mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco
In Collection: Mask R-CNN
Config: configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
Metadata:
Training Memory (GB): 11.9
Epochs: 36
Training Data: COCO
Training Techniques:
- AdamW
Training ... | 4,301 | 34.553719 | 190 | yml |
mmdetection | mmdetection-master/configs/swin/retinanet_swin-t-p4-w7_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'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... | 1,073 | 33.645161 | 123 | py |
mmdetection | mmdetection-master/configs/timm_example/README.md | # Timm Example
> [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models)
<!-- [OTHERS] -->
## Abstract
Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts tha... | 2,623 | 40.650794 | 300 | md |
mmdetection | mmdetection-master/configs/timm_example/retinanet_timm_efficientnet_b1_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'
]
# please install mmcls>=0.20.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_f... | 679 | 31.380952 | 75 | py |
mmdetection | mmdetection-master/configs/timm_example/retinanet_timm_tv_resnet50_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'
]
# please install mmcls>=0.20.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_f... | 666 | 32.35 | 75 | py |
mmdetection | mmdetection-master/configs/tood/README.md | # TOOD
> [TOOD: Task-aligned One-stage Object Detection](https://arxiv.org/abs/2108.07755)
<!-- [ALGORITHM] -->
## Abstract
One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain le... | 6,820 | 165.365854 | 1,244 | md |
mmdetection | mmdetection-master/configs/tood/metafile.yml | Collections:
- Name: TOOD
Metadata:
Training Data: COCO
Training Techniques:
- SGD
Training Resources: 8x V100 GPUs
Architecture:
- TOOD
Paper:
URL: https://arxiv.org/abs/2108.07755
Title: 'TOOD: Task-aligned One-stage Object Detection'
README: configs/t... | 3,206 | 32.40625 | 178 | yml |
mmdetection | mmdetection-master/configs/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py | _base_ = './tood_r101_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(num_dcn=2))
| 241 | 29.25 | 78 | py |
mmdetection | mmdetection-master/configs/tood/tood_r101_fpn_mstrain_2x_coco.py | _base_ = './tood_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 24.25 | 61 | py |
mmdetection | mmdetection-master/configs/tood/tood_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='TOOD',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... | 2,306 | 29.76 | 79 | py |
mmdetection | mmdetection-master/configs/tood/tood_r50_fpn_anchor_based_1x_coco.py | _base_ = './tood_r50_fpn_1x_coco.py'
model = dict(bbox_head=dict(anchor_type='anchor_based'))
| 94 | 30.666667 | 56 | py |
mmdetection | mmdetection-master/configs/tood/tood_r50_fpn_mstrain_2x_coco.py | _base_ = './tood_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
# multi-scale training
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'),
... | 789 | 33.347826 | 77 | py |
mmdetection | mmdetection-master/configs/tood/tood_x101_64x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py | _base_ = './tood_x101_64x4d_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True),
),
bbox_head=dict(num_dcn=2))
| 253 | 30.75 | 78 | py |
mmdetection | mmdetection-master/configs/tood/tood_x101_64x4d_fpn_mstrain_2x_coco.py | _base_ = './tood_r50_fpn_mstrain_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),
norm_eval=True... | 447 | 25.352941 | 76 | py |
mmdetection | mmdetection-master/configs/tridentnet/README.md | # TridentNet
> [Scale-Aware Trident Networks for Object Detection](https://arxiv.org/abs/1901.01892)
<!-- [ALGORITHM] -->
## Abstract
Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale varia... | 3,931 | 99.820513 | 986 | md |
mmdetection | mmdetection-master/configs/tridentnet/metafile.yml | Collections:
- Name: TridentNet
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNet
- TridentNet Block
Paper:
URL: https://arxiv.org/abs/1901.01892
Titl... | 1,921 | 33.321429 | 174 | yml |
mmdetection | mmdetection-master/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='TridentFasterRCNN',
backbone=dict(
type='TridentResNet',
trident_dilations=(1, 2, 3),
... | 1,868 | 32.375 | 74 | py |
mmdetection | mmdetection-master/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py | _base_ = 'tridentnet_r50_caffe_1x_coco.py'
# use caffe img_norm
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='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(133... | 756 | 31.913043 | 72 | py |
mmdetection | mmdetection-master/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py | _base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py'
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 138 | 26.8 | 53 | py |
mmdetection | mmdetection-master/configs/vfnet/README.md | # VarifocalNet
> [VarifocalNet: An IoU-aware Dense Object Detector](https://arxiv.org/abs/2008.13367)
<!-- [ALGORITHM] -->
## Abstract
Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combinati... | 8,844 | 179.510204 | 1,350 | md |
mmdetection | mmdetection-master/configs/vfnet/metafile.yml | Collections:
- Name: VFNet
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- Varifocal Loss
Paper:
URL: https://arxiv.org/abs/2008.13367
... | 4,075 | 33.837607 | 191 | yml |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r101_fpn_1x_coco.py | _base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 193 | 26.714286 | 61 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r101_fpn_2x_coco.py | _base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 279 | 30.111111 | 61 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
... | 546 | 33.1875 | 74 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 27.857143 | 61 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
n... | 602 | 30.736842 | 74 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True... | 464 | 26.352941 | 62 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='VFNet',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 3,240 | 29.009259 | 79 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(dcn_on_last_conv=True))
| 248 | 34.571429 | 74 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_1x_coco.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'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 480), (1333, 960)],... | 1,312 | 31.825 | 77 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_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),
n... | 585 | 31.555556 | 76 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_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),
norm_eval=True... | 447 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_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),
n... | 585 | 31.555556 | 76 | py |
mmdetection | mmdetection-master/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_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),
norm_eval=True... | 447 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/wider_face/README.md | # WIDER FACE
> [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523)
<!-- [DATASET] -->
## Abstract
Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that the... | 2,669 | 45.034483 | 1,000 | md |
mmdetection | mmdetection-master/configs/wider_face/ssd300_wider_face.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=1))
# optimizer
optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learni... | 557 | 28.368421 | 71 | py |
mmdetection | mmdetection-master/configs/yolact/README.md | # YOLACT
> [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689)
<!-- [ALGORITHM] -->
## Abstract
We present a simple, fully-convolutional model for real-time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than ... | 4,963 | 64.315789 | 1,034 | md |