repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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|---|---|---|---|---|---|---|
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 226 | 31.428571 | 70 | py |
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 195 | 31.666667 | 70 | py |
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 252 | 35.142857 | 96 | py |
ERD | ERD-main/configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py | _base_ = './cascade-mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 222 | 36.166667 | 93 | py |
ERD | ERD-main/configs/tood/tood_r101_fpn_ms-2x_coco.py | _base_ = './tood_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 196 | 23.625 | 61 | py |
ERD | ERD-main/configs/tood/tood_x101-64x4d_fpn_ms-2x_coco.py | _base_ = './tood_r50_fpn_ms-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,
... | 442 | 25.058824 | 76 | py |
ERD | ERD-main/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 |
ERD | ERD-main/configs/tood/tood_x101-64x4d-dconv-c4-c5_fpn_ms-2x_coco.py | _base_ = './tood_x101-64x4d_fpn_ms-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))
| 248 | 30.125 | 78 | py |
ERD | ERD-main/configs/tood/tood_r101-dconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './tood_r101_fpn_ms-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))
| 236 | 28.625 | 78 | py |
ERD | ERD-main/configs/tood/tood_r50_fpn_ms-2x_coco.py | _base_ = './tood_r50_fpn_1x_coco.py'
max_epochs = 24
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
g... | 801 | 24.870968 | 79 | py |
ERD | ERD-main/configs/tood/tood_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='TOOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 2,482 | 29.654321 | 79 | py |
ERD | ERD-main/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb... | 2,213 | 29.328767 | 79 | py |
ERD | ERD-main/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_ms-2x_coco.py | _base_ = [
'../_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_large_patch4_window12_384_22k.pth' # noqa
model = dict(
type='ATSS',
data_preprocessor=dict(
... | 4,605 | 31.666667 | 129 | py |
ERD | ERD-main/configs/dyhead/atss_r50-caffe_fpn_dyhead_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False... | 3,366 | 31.375 | 78 | py |
ERD | ERD-main/configs/gn+ws/faster-rcnn_x50-32x4d_fpn_gn-ws-all_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 544 | 27.684211 | 66 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_x101-32x4d_fpn_gn-ws-all_20-23-24e_coco.py | _base_ = './mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... | 412 | 21.944444 | 79 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_r101_fpn_gn-ws-all_2x_coco.py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
| 207 | 28.714286 | 79 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
# model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=... | 559 | 27 | 66 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
# model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices... | 561 | 27.1 | 67 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_r50_fpn_gn-ws-all_20-23-24e_coco.py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=... | 405 | 21.555556 | 79 | py |
ERD | ERD-main/configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/... | 577 | 33 | 78 | py |
ERD | ERD-main/configs/gn+ws/faster-rcnn_r101_fpn_gn-ws-all_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
| 209 | 29 | 79 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_r101_fpn_gn-ws-all_20-23-24e_coco.py | _base_ = './mask-rcnn_r101_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end... | 406 | 21.611111 | 79 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_x50-32x4d_fpn_gn-ws-all_20-23-24e_coco.py | _base_ = './mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... | 411 | 21.888889 | 79 | py |
ERD | ERD-main/configs/gn+ws/mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resn... | 988 | 28.088235 | 79 | py |
ERD | ERD-main/configs/gn+ws/faster-rcnn_x101-32x4d_fpn_gn-ws-all_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 546 | 27.789474 | 67 | py |
ERD | ERD-main/configs/guided_anchoring/ga-rpn_x101-64x4d_fpn_1x_coco.py | _base_ = './ga-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',
... | 416 | 26.8 | 76 | py |
ERD | ERD-main/configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50-caffe_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='GARetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
... | 2,032 | 31.790323 | 74 | py |
ERD | ERD-main/configs/guided_anchoring/ga-rpn_r101-caffe_fpn_1x_coco.py | _base_ = './ga-rpn_r50-caffe_fpn_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 236 | 25.333333 | 67 | py |
ERD | ERD-main/configs/guided_anchoring/ga-retinanet_r101-caffe_fpn_1x_coco.py | _base_ = './ga-retinanet_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 225 | 27.25 | 67 | py |
ERD | ERD-main/configs/guided_anchoring/ga-retinanet_x101-32x4d_fpn_1x_coco.py | _base_ = './ga-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'... | 422 | 27.2 | 76 | py |
ERD | ERD-main/configs/guided_anchoring/ga-faster-rcnn_r101-caffe_fpn_1x_coco.py | _base_ = './ga-faster-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 227 | 27.5 | 67 | py |
ERD | ERD-main/configs/guided_anchoring/ga-retinanet_x101-64x4d_fpn_1x_coco.py | _base_ = './ga-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'... | 422 | 27.2 | 76 | py |
ERD | ERD-main/configs/guided_anchoring/ga-rpn_r50-caffe_fpn_1x_coco.py | _base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,... | 2,005 | 33.586207 | 74 | py |
ERD | ERD-main/configs/guided_anchoring/ga-retinanet_r101-caffe_fpn_ms-2x.py | _base_ = './ga-retinanet_r101-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='RandomFlip... | 869 | 23.857143 | 73 | py |
ERD | ERD-main/configs/guided_anchoring/ga-faster-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './ga-faster-rcnn_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='pytorc... | 424 | 27.333333 | 76 | py |
ERD | ERD-main/configs/guided_anchoring/ga-faster-rcnn_r50_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per... | 2,379 | 35.615385 | 77 | py |
ERD | ERD-main/configs/guided_anchoring/ga-faster-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scal... | 2,385 | 35.707692 | 77 | py |
ERD | ERD-main/configs/guided_anchoring/ga-fast-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
in... | 2,441 | 35.447761 | 78 | py |
ERD | ERD-main/configs/guided_anchoring/ga-faster-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './ga-faster-rcnn_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='pytorc... | 424 | 27.333333 | 76 | py |
ERD | ERD-main/configs/guided_anchoring/ga-rpn_r50_fpn_1x_coco.py | _base_ = '../rpn/rpn_r50_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,
... | 1,999 | 33.482759 | 74 | py |
ERD | ERD-main/configs/guided_anchoring/ga-retinanet_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='GARetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
... | 2,026 | 31.693548 | 74 | py |
ERD | ERD-main/configs/guided_anchoring/ga-rpn_x101-32x4d_fpn_1x_coco.py | _base_ = './ga-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',
... | 416 | 26.8 | 76 | py |
ERD | ERD-main/configs/solov2/solov2-light_r50_fpn_ms-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)))
# dataset settings
train_pipeline = [
dict(... | 1,623 | 27.491228 | 77 | py |
ERD | ERD-main/configs/solov2/solov2-light_r18_fpn_ms-3x_coco.py | _base_ = './solov2-light_r50_fpn_ms-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]))
| 218 | 26.375 | 70 | py |
ERD | ERD-main/configs/solov2/solov2_r101_fpn_ms-3x_coco.py | _base_ = './solov2_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101, init_cfg=dict(checkpoint='torchvision://resnet101')))
| 166 | 22.857143 | 72 | py |
ERD | ERD-main/configs/solov2/solov2-light_r50-dcn_fpn_ms-3x_coco.py | _base_ = './solov2-light_r50_fpn_ms-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_range... | 525 | 34.066667 | 78 | py |
ERD | ERD-main/configs/solov2/solov2_r101-dcn_fpn_ms-3x_coco.py | _base_ = './solov2_r50_fpn_ms-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=dic... | 457 | 31.714286 | 78 | py |
ERD | ERD-main/configs/solov2/solov2_x101-dcn_fpn_ms-3x_coco.py | _base_ = './solov2_r50_fpn_ms-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_c... | 560 | 30.166667 | 78 | py |
ERD | ERD-main/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',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],... | 2,046 | 27.830986 | 78 | py |
ERD | ERD-main/configs/solov2/solov2_r50_fpn_ms-3x_coco.py | _base_ = './solov2_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
... | 905 | 24.166667 | 73 | py |
ERD | ERD-main/configs/solov2/solov2-light_r34_fpn_ms-3x_coco.py | _base_ = './solov2-light_r50_fpn_ms-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]))
| 218 | 26.375 | 70 | py |
ERD | ERD-main/configs/dab_detr/dab-detr_r50_8xb2-50e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DABDETR',
num_queries=300,
with_random_refpoints=False,
num_patterns=0,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395,... | 5,406 | 32.79375 | 79 | py |
ERD | ERD-main/configs/convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-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'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mm... | 3,397 | 34.030928 | 162 | py |
ERD | ERD-main/configs/convnext/cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_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'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_modu... | 5,665 | 35.554839 | 162 | py |
ERD | ERD-main/configs/convnext/cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py | _base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py' # noqa
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=Fals... | 965 | 34.777778 | 163 | py |
ERD | ERD-main/configs/lad/lad_r101-paa-r50_fpn_2xb8_coco_1x.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa
model = dict(
type='LAD',
data_preprocesso... | 3,956 | 29.914063 | 138 | py |
ERD | ERD-main/configs/lad/lad_r50-paa-r101_fpn_2xb8_coco_1x.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' # noqa
model = dict(
type='LAD',
data_preprocess... | 3,934 | 29.984252 | 139 | py |
ERD | ERD-main/configs/sparse_rcnn/sparse-rcnn_r101_fpn_ms-480-800-3x_coco.py | _base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 211 | 25.5 | 61 | py |
ERD | ERD-main/configs/sparse_rcnn/sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py | _base_ = './sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 230 | 27.875 | 73 | py |
ERD | ERD-main/configs/sparse_rcnn/sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py | _base_ = './sparse-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(60... | 953 | 27.909091 | 79 | py |
ERD | ERD-main/configs/sparse_rcnn/sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py | _base_ = './sparse-rcnn_r50_fpn_ms-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)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(type='LoadImageFr... | 1,845 | 40.954545 | 75 | py |
ERD | ERD-main/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',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[5... | 3,572 | 34.029412 | 79 | py |
ERD | ERD-main/configs/cityscapes/mask-rcnn_r50_fpn_1x_cityscapes.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_instance.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
... | 1,354 | 29.795455 | 153 | py |
ERD | ERD-main/configs/cityscapes/faster-rcnn_r50_fpn_1x_cityscapes.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
num_classes=8,
loss_bb... | 1,286 | 29.642857 | 159 | py |
ERD | ERD-main/configs/deepfashion/mask-rcnn_r50_fpn_15e_deepfashion.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
# runtime settings
max_epochs = 15
train_c... | 663 | 26.666667 | 79 | py |
ERD | ERD-main/configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=di... | 1,003 | 26.135135 | 79 | py |
ERD | ERD-main/configs/gn/mask-rcnn_r50-contrib_fpn_gn-all_3x_coco.py | _base_ = './mask-rcnn_r50-contrib_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... | 411 | 20.684211 | 79 | py |
ERD | ERD-main/configs/gn/mask-rcnn_r50-contrib_fpn_gn-all_2x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')),
neck=dict(norm_cfg=norm_cfg),
roi_... | 863 | 26 | 79 | py |
ERD | ERD-main/configs/gn/mask-rcnn_r50_fpn_gn-all_3x_coco.py | _base_ = './mask-rcnn_r50_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=ma... | 403 | 20.263158 | 79 | py |
ERD | ERD-main/configs/gn/mask-rcnn_r101_fpn_gn-all_2x_coco.py | _base_ = './mask-rcnn_r50_fpn_gn-all_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_gn')))
| 219 | 26.5 | 63 | py |
ERD | ERD-main/configs/gn/mask-rcnn_r101_fpn_gn-all_3x_coco.py | _base_ = './mask-rcnn_r101_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=m... | 404 | 20.315789 | 79 | py |
ERD | ERD-main/docs/en/stat.py | #!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/main/configs'
files = sorted(glob.glob('../../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(... | 1,537 | 22.661538 | 74 | py |
ERD | ERD-main/docs/en/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/main/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If exte... | 3,435 | 28.367521 | 79 | py |
ERD | ERD-main/docs/zh_cn/stat.py | #!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/main/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f.replace(... | 1,517 | 22.353846 | 74 | py |
ERD | ERD-main/docs/zh_cn/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 3,459 | 28.07563 | 79 | py |
ERD | ERD-main/data_process/select_cats.py | import argparse
import json
import time
import os.path as osp
def arg_parse():
parser = argparse.ArgumentParser(description='COCO Dataset Loader')
parser.add_argument('--dataset', default='COCO', help='dataset type')
parser.add_argument('--data_path',
default='/home/bl/Documents/P... | 2,243 | 31.521739 | 114 | py |
ERD | ERD-main/mmdet/registry.py | # Copyright (c) OpenMMLab. All rights reserved.
"""MMDetection provides 17 registry nodes to support using modules across
projects. Each node is a child of the root registry in MMEngine.
More details can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from mmengine.registry import D... | 4,875 | 38.967213 | 78 | py |
ERD | ERD-main/mmdet/version.py | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is parsed... | 795 | 27.428571 | 72 | py |
ERD | ERD-main/mmdet/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.7.1'
mmengi... | 1,042 | 36.25 | 76 | py |
ERD | ERD-main/mmdet/apis/inference.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import torch
import torch.nn as nn
from mmcv.ops import RoIPool
from mmcv.transforms import Compose
from mmengine.config import Config
from mmengine.model... | 8,007 | 33.222222 | 79 | py |
ERD | ERD-main/mmdet/apis/det_inferencer.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import warnings
from typing import Dict, Iterable, List, Optional, Sequence, Union
import mmcv
import mmengine
import numpy as np
import torch.nn as nn
from mmengine.dataset import Compose
from mmengine.fileio import (get_file_backend, i... | 24,008 | 39.624365 | 79 | py |
ERD | ERD-main/mmdet/apis/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .det_inferencer import DetInferencer
from .inference import (async_inference_detector, inference_detector,
init_detector)
__all__ = [
'init_detector', 'async_inference_detector', 'inference_detector',
'DetInferencer'
]
| 305 | 29.6 | 70 | py |
ERD | ERD-main/mmdet/evaluation/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .functional import * # noqa: F401,F403
from .metrics import * # noqa: F401,F403
| 135 | 33 | 47 | py |
ERD | ERD-main/mmdet/evaluation/metrics/dump_proposals_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from typing import Optional, Sequence
from mmengine.dist import is_main_process
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump
from mmengine.logging import MMLogger
from mmengine.structures import InstanceData
... | 4,984 | 40.541667 | 125 | py |
ERD | ERD-main/mmdet/evaluation/metrics/coco_occluded_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Union
import mmengine
import numpy as np
from mmengine.fileio import load
from mmengine.logging import print_log
from pycocotools import mask as coco_mask
from terminaltables import AsciiTable
from mmdet.registry import METRICS
f... | 8,370 | 39.834146 | 99 | py |
ERD | ERD-main/mmdet/evaluation/metrics/coco_panoptic_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import itertools
import os.path as osp
import tempfile
from typing import Dict, Optional, Sequence, Tuple, Union
import mmcv
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump, get_local_path, load
from mmengine.... | 24,814 | 39.48124 | 125 | py |
ERD | ERD-main/mmdet/evaluation/metrics/coco_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import itertools
import os.path as osp
import tempfile
from collections import OrderedDict
from typing import Dict, List, Optional, Sequence, Union
import numpy as np
import torch
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump,... | 25,325 | 41.852792 | 125 | py |
ERD | ERD-main/mmdet/evaluation/metrics/crowdhuman_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import json
import os.path as osp
import tempfile
from collections import OrderedDict
from multiprocessing import Process, Queue
from typing import Dict, List, Optional, Sequence, Union
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengin... | 32,675 | 38.607273 | 125 | py |
ERD | ERD-main/mmdet/evaluation/metrics/cityscapes_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import shutil
import tempfile
from collections import OrderedDict
from typing import Dict, Optional, Sequence
import mmcv
import numpy as np
from mmengine.dist import is_main_process
from mmengine.evaluator import BaseMetric
from mmengine.... | 8,769 | 41.572816 | 125 | py |
ERD | ERD-main/mmdet/evaluation/metrics/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_occluded_metric import CocoOccludedSeparatedMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .crowdhuman_metric import CrowdHumanMetric
from .dump_det_results im... | 722 | 39.166667 | 79 | py |
ERD | ERD-main/mmdet/evaluation/metrics/openimages_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from collections import OrderedDict
from typing import List, Optional, Sequence, Union
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengine.logging import MMLogger, print_log
from mmdet.registry import METRICS
from ..functional import e... | 10,709 | 44 | 79 | py |
ERD | ERD-main/mmdet/evaluation/metrics/dump_det_results.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Sequence
from mmengine.evaluator import DumpResults
from mmengine.evaluator.metric import _to_cpu
from mmdet.registry import METRICS
from mmdet.structures.mask import encode_mask_results
@METRICS.register_module()
class DumpDetResult... | 1,888 | 38.354167 | 78 | py |
ERD | ERD-main/mmdet/evaluation/metrics/lvis_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import itertools
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
from typing import Dict, List, Optional, Sequence, Union
import numpy as np
from mmengine.fileio import get_local_path
from mmengine.logging import MMLogger
from te... | 15,924 | 42.630137 | 149 | py |
ERD | ERD-main/mmdet/evaluation/metrics/voc_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from collections import OrderedDict
from typing import List, Optional, Sequence, Union
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengine.logging import MMLogger
from mmdet.registry import METRICS
from ..functional imp... | 8,080 | 44.655367 | 90 | py |
ERD | ERD-main/mmdet/evaluation/functional/class_names.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils import is_str
def wider_face_classes() -> list:
"""Class names of WIDERFace."""
return ['face']
def voc_classes() -> list:
"""Class names of PASCAL VOC."""
return [
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'c... | 32,936 | 62.584942 | 79 | py |
ERD | ERD-main/mmdet/evaluation/functional/recall.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Sequence
import numpy as np
from mmengine.logging import print_log
from terminaltables import AsciiTable
from .bbox_overlaps import bbox_overlaps
def _recalls(all_ious, proposal_nums, thrs):
img_num = all_ious.shape[0]
total_gt_num... | 6,848 | 33.245 | 79 | py |
ERD | ERD-main/mmdet/evaluation/functional/cityscapes_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) https://github.com/mcordts/cityscapesScripts
# A wrapper of `cityscapesscripts` which supports loading groundtruth
# image from `backend_args`.
import json
import os
import sys
from pathlib import Path
from typing import Optional, Union
import mmcv
import... | 11,654 | 37.465347 | 97 | py |
ERD | ERD-main/mmdet/evaluation/functional/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .bbox_overlaps import bbox_overlaps
from .cityscapes_utils import evaluateImgLists
from .class_names import (cityscapes_classes, coco_classes,
coco_panoptic_classes, dataset_aliases, get_classes,
imagenet_det_classe... | 1,331 | 52.28 | 78 | py |
ERD | ERD-main/mmdet/evaluation/functional/panoptic_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) 2018, Alexander Kirillov
# This file supports `backend_args` for `panopticapi`,
# the source code is copied from `panopticapi`,
# only the way to load the gt images is modified.
import multiprocessing
import os
import mmcv
import numpy as np
from mmengin... | 9,128 | 38.864629 | 79 | py |
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