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/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'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in m... | 712 | 30 | 75 | py |
ERD | ERD-main/configs/panoptic_fpn/panoptic-fpn_r50_fpn_ms-3x_coco.py | _base_ = './panoptic-fpn_r50_fpn_1x_coco.py'
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(... | 939 | 25.111111 | 79 | py |
ERD | ERD-main/configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_panoptic.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PanopticFPN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
... | 1,400 | 29.456522 | 73 | py |
ERD | ERD-main/configs/panoptic_fpn/panoptic-fpn_r101_fpn_1x_coco.py | _base_ = './panoptic-fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 200 | 27.714286 | 61 | py |
ERD | ERD-main/configs/panoptic_fpn/panoptic-fpn_r101_fpn_ms-3x_coco.py | _base_ = './panoptic-fpn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 203 | 28.142857 | 61 | py |
ERD | ERD-main/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 |
ERD | ERD-main/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 |
ERD | ERD-main/configs/scnet/scnet_x101-64x4d_fpn_8xb1-20e_coco.py | _base_ = './scnet_x101-64x4d_fpn_20e_coco.py'
train_dataloader = dict(batch_size=1, num_workers=1)
optim_wrapper = dict(optimizer=dict(lr=0.01))
# 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_scale_lr = dict(base_bat... | 331 | 35.888889 | 56 | py |
ERD | ERD-main/configs/scnet/scnet_r50_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
max_epochs = 20
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, 19],
... | 374 | 22.4375 | 79 | py |
ERD | ERD-main/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=... | 5,063 | 35.431655 | 79 | py |
ERD | ERD-main/configs/cascade_rpn/cascade-rpn_fast-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = '../fast_rcnn/fast-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
loss_bbox=dict(type=... | 1,278 | 44.678571 | 110 | py |
ERD | ERD-main/configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
fea... | 3,404 | 36.833333 | 79 | py |
ERD | ERD-main/configs/cascade_rpn/cascade-rpn_r50-caffe_fpn_1x_coco.py | _base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
a... | 2,727 | 34.428571 | 79 | py |
ERD | ERD-main/configs/legacy_1.x/faster-rcnn_r50_fpn_1x_coco_v1.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(
type='FasterRCNN',
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
rp... | 1,385 | 34.538462 | 79 | py |
ERD | ERD-main/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
type='RetinaHead',
anchor_generator=dict(
type='LegacyAnchorGenerator',
... | 617 | 33.333333 | 73 | py |
ERD | ERD-main/configs/legacy_1.x/ssd300_coco_v1.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
input_size = 300
model = dict(
bbox_head=dict(
type='SSDHead',
anchor_generator=dict(
type='LegacySSDAnchorGene... | 709 | 32.809524 | 73 | py |
ERD | ERD-main/configs/legacy_1.x/retinanet_r50-caffe_fpn_1x_coco_v1.py | _base_ = './retinanet_r50_fpn_1x_coco_v1.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
# use caffe img_norm
mean=[102.9801, 115.9465, 122.7717],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=di... | 512 | 29.176471 | 65 | py |
ERD | ERD-main/configs/legacy_1.x/mask-rcnn_r50_fpn_1x_coco_v1.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(
rpn_head=dict(
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
bbox_coder=dict(type='Le... | 1,238 | 34.4 | 79 | py |
ERD | ERD-main/configs/legacy_1.x/cascade-mask-rcnn_r50_fpn_1x_coco_v1.py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... | 2,744 | 33.746835 | 79 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_r101-caffe_fpn_2x_coco.py | _base_ = './ms-rcnn_r101-caffe_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | 433 | 23.111111 | 79 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_x101-64x4d_fpn_2x_coco.py | _base_ = './ms-rcnn_x101-64x4d_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | 433 | 23.111111 | 79 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_r50_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
... | 509 | 29 | 52 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './ms-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='pytorch',
... | 417 | 26.866667 | 76 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './ms-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='pytorch',
... | 417 | 26.866667 | 76 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_2x_coco.py | _base_ = './ms-rcnn_r50-caffe_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | 432 | 23.055556 | 79 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256... | 515 | 29.352941 | 58 | py |
ERD | ERD-main/configs/ms_rcnn/ms-rcnn_r101-caffe_fpn_1x_coco.py | _base_ = './ms-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 220 | 26.625 | 67 | py |
ERD | ERD-main/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 |
ERD | ERD-main/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), (192, 76... | 774 | 30 | 78 | py |
ERD | ERD-main/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... | 1,718 | 32.705882 | 78 | py |
ERD | ERD-main/configs/solo/solo_r50_fpn_3x_coco.py | _base_ = './solo_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),
... | 918 | 24.527778 | 73 | py |
ERD | ERD-main/configs/solo/solo_r101_fpn_8xb8-lsj-200e_coco.py | _base_ = './solo_r50_fpn_8xb8-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 204 | 24.625 | 61 | py |
ERD | ERD-main/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',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 1,817 | 27.857143 | 78 | py |
ERD | ERD-main/configs/solo/solo_r50_fpn_8xb8-lsj-200e_coco.py | _base_ = '../common/lsj-200e_coco-instance.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# model settings
model = dict(
type='SOLO',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12... | 2,213 | 29.75 | 78 | py |
ERD | ERD-main/configs/solo/solo_r18_fpn_8xb8-lsj-200e_coco.py | _base_ = './solo_r50_fpn_8xb8-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 228 | 27.625 | 79 | py |
ERD | ERD-main/configs/fast_rcnn/fast-rcnn_r101_fpn_2x_coco.py | _base_ = './fast-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
ERD | ERD-main/configs/fast_rcnn/fast-rcnn_r50_fpn_2x_coco.py | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
train_cfg = dict(max_epochs=24)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
milestones=[16, 22],
gamm... | 329 | 21 | 79 | py |
ERD | ERD-main/configs/fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/fast-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadProposals', num_max_proposals=200... | 1,353 | 32.85 | 73 | py |
ERD | ERD-main/configs/fast_rcnn/fast-rcnn_r101-caffe_fpn_1x_coco.py | _base_ = './fast-rcnn_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 |
ERD | ERD-main/configs/fast_rcnn/fast-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False)... | 490 | 27.882353 | 66 | py |
ERD | ERD-main/configs/fast_rcnn/fast-rcnn_r101_fpn_1x_coco.py | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
ERD | ERD-main/configs/hrnet/fcos_hrnetv2p-w40-gn-head_ms-640-800-4xb4-2x_coco.py | _base_ = './fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=di... | 480 | 39.083333 | 78 | py |
ERD | ERD-main/configs/hrnet/cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,528 | 28.403846 | 79 | py |
ERD | ERD-main/configs/hrnet/htc_x101-64x4d_fpn_16xb1-28e_coco.py | _base_ = '../htc/htc_x101-64x4d_fpn_16xb1-20e_coco.py'
# learning policy
max_epochs = 28
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 392 | 22.117647 | 79 | py |
ERD | ERD-main/configs/hrnet/mask-rcnn_hrnetv2p-w40-2x_coco.py | _base_ = './mask-rcnn_hrnetv2p-w40_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | 384 | 21.647059 | 79 | py |
ERD | ERD-main/configs/hrnet/mask-rcnn_hrnetv2p-w18-1x_coco.py | _base_ = './mask-rcnn_hrnetv2p-w32-1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoi... | 436 | 38.727273 | 77 | py |
ERD | ERD-main/configs/hrnet/cascade-rcnn_hrnetv2p-w18-20e_coco.py | _base_ = './cascade-rcnn_hrnetv2p-w32-20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='... | 457 | 37.166667 | 77 | py |
ERD | ERD-main/configs/hrnet/mask-rcnn_hrnetv2p-w32-2x_coco.py | _base_ = './mask-rcnn_hrnetv2p-w32-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | 384 | 21.647059 | 79 | py |
ERD | ERD-main/configs/hrnet/cascade-rcnn_hrnetv2p-w40-20e_coco.py | _base_ = './cascade-rcnn_hrnetv2p-w32-20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=... | 482 | 36.153846 | 78 | py |
ERD | ERD-main/configs/hrnet/fcos_hrnetv2p-w18-gn-head_ms-640-800-4xb4-2x_coco.py | _base_ = './fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pr... | 455 | 40.454545 | 77 | py |
ERD | ERD-main/configs/hrnet/mask-rcnn_hrnetv2p-w32-1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,181 | 30.105263 | 76 | py |
ERD | ERD-main/configs/hrnet/faster-rcnn_hrnetv2p-w32-1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,185 | 30.210526 | 76 | py |
ERD | ERD-main/configs/hrnet/htc_hrnetv2p-w40_28e_coco.py | _base_ = './htc_hrnetv2p-w40_20e_coco.py'
# learning policy
max_epochs = 28
train_cfg = dict(max_epochs=max_epochs)
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_epo... | 379 | 21.352941 | 79 | py |
ERD | ERD-main/configs/hrnet/htc_hrnetv2p-w32_20e_coco.py | _base_ = '../htc/htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_ch... | 1,170 | 29.815789 | 76 | py |
ERD | ERD-main/configs/hrnet/fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'
model = dict(
data_preprocessor=dict(
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnot... | 933 | 24.944444 | 79 | py |
ERD | ERD-main/configs/hrnet/mask-rcnn_hrnetv2p-w18-2x_coco.py | _base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | 384 | 21.647059 | 79 | py |
ERD | ERD-main/configs/hrnet/cascade-rcnn_hrnetv2p-w32-20e_coco.py | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,523 | 28.307692 | 79 | py |
ERD | ERD-main/configs/hrnet/faster-rcnn_hrnetv2p-w40-1x_coco.py | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
t... | 463 | 37.666667 | 78 | py |
ERD | ERD-main/configs/hrnet/faster-rcnn_hrnetv2p-w32_2x_coco.py | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 386 | 21.764706 | 79 | py |
ERD | ERD-main/configs/hrnet/faster-rcnn_hrnetv2p-w18-2x_coco.py | _base_ = './faster-rcnn_hrnetv2p-w18-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 386 | 21.764706 | 79 | py |
ERD | ERD-main/configs/hrnet/cascade-mask-rcnn_hrnetv2p-w18_20e_coco.py | _base_ = './cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
t... | 462 | 37.583333 | 77 | py |
ERD | ERD-main/configs/hrnet/htc_hrnetv2p-w40_20e_coco.py | _base_ = './htc_hrnetv2p-w32_20e_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pr... | 456 | 37.083333 | 78 | py |
ERD | ERD-main/configs/hrnet/htc_hrnetv2p-w18_20e_coco.py | _base_ = './htc_hrnetv2p-w32_20e_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='o... | 431 | 38.272727 | 77 | py |
ERD | ERD-main/configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pr... | 455 | 37 | 77 | py |
ERD | ERD-main/configs/hrnet/faster-rcnn_hrnetv2p-w40_2x_coco.py | _base_ = './faster-rcnn_hrnetv2p-w40-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 386 | 21.764706 | 79 | py |
ERD | ERD-main/configs/hrnet/fcos_hrnetv2p-w18-gn-head_4xb4-2x_coco.py | _base_ = './fcos_hrnetv2p-w18-gn-head_4xb4-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 392 | 22.117647 | 79 | py |
ERD | ERD-main/configs/hrnet/fcos_hrnetv2p-w18-gn-head_4xb4-1x_coco.py | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', ... | 444 | 39.454545 | 77 | py |
ERD | ERD-main/configs/hrnet/cascade-mask-rcnn_hrnetv2p-w40-20e_coco.py | _base_ = './cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init... | 487 | 36.538462 | 78 | py |
ERD | ERD-main/configs/hrnet/fcos_hrnetv2p-w32-gn-head_4xb4-2x_coco.py | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 392 | 22.117647 | 79 | py |
ERD | ERD-main/configs/hrnet/mask-rcnn_hrnetv2p-w40_1x_coco.py | _base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
typ... | 461 | 37.5 | 78 | py |
ERD | ERD-main/configs/hrnet/fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py | _base_ = '../fcos/fcos_r50-caffe_fpn_gn-head_4xb4-1x_coco.py'
model = dict(
data_preprocessor=dict(
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
... | 1,360 | 29.931818 | 76 | py |
ERD | ERD-main/configs/empirical_attention/faster-rcnn_r50-attn0010_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
... | 403 | 27.857143 | 56 | py |
ERD | ERD-main/configs/empirical_attention/faster-rcnn_r50-attn1111-dcn_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
... | 575 | 32.882353 | 72 | py |
ERD | ERD-main/configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
... | 403 | 27.857143 | 56 | py |
ERD | ERD-main/configs/empirical_attention/faster-rcnn_r50-attn0010-dcn_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
... | 575 | 32.882353 | 72 | py |
ERD | ERD-main/configs/yolox/yolox_s_8xb8-300e_coco.py | _base_ = [
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./yolox_tta.py'
]
img_scale = (640, 640) # width, height
# model settings
model = dict(
type='YOLOX',
data_preprocessor=dict(
type='DetDataPreprocessor',
pad_size_divisor=32,
batch_augments=[
... | 7,648 | 29.474104 | 78 | py |
ERD | ERD-main/configs/yolox/yolox_m_8xb8-300e_coco.py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.67, widen_factor=0.75),
neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2),
bbox_head=dict(in_channels=192, feat_channels=192),
)
| 267 | 28.777778 | 79 | py |
ERD | ERD-main/configs/yolox/yolox_x_8xb8-300e_coco.py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.33, widen_factor=1.25),
neck=dict(
in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4),
bbox_head=dict(in_channels=320, feat_channels=320))
| 275 | 29.666667 | 74 | py |
ERD | ERD-main/configs/yolox/yolox_l_8xb8-300e_coco.py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.0, widen_factor=1.0),
neck=dict(
in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3),
bbox_head=dict(in_channels=256, feat_channels=256))
| 273 | 29.444444 | 74 | py |
ERD | ERD-main/configs/yolox/yolox_tta.py | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... | 1,240 | 32.540541 | 78 | py |
ERD | ERD-main/configs/yolox/yolox_nano_8xb8-300e_coco.py | _base_ = './yolox_tiny_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True),
neck=dict(
in_channels=[64, 128, 256],
out_channels=64,
num_csp_blocks=1,
use_depthwise=True),
bbox_head=dict(in_channels=64, fe... | 357 | 28.833333 | 77 | py |
ERD | ERD-main/configs/yolox/yolox_tiny_8xb8-300e_coco.py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_f... | 1,829 | 32.272727 | 76 | py |
ERD | ERD-main/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... | 1,648 | 26.032787 | 123 | py |
ERD | ERD-main/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... | 1,058 | 32.09375 | 123 | py |
ERD | ERD-main/configs/swin/mask-rcnn_swin-t-p4-w7_fpn_amp-ms-crop-3x_coco.py | _base_ = './mask-rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 170 | 41.75 | 65 | py |
ERD | ERD-main/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... | 3,297 | 31.98 | 123 | py |
ERD | ERD-main/configs/swin/mask-rcnn_swin-s-p4-w7_fpn_amp-ms-crop-3x_coco.py | _base_ = './mask-rcnn_swin-t-p4-w7_fpn_amp-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)))
| 317 | 44.428571 | 124 | py |
ERD | ERD-main/configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-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',
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncB... | 1,201 | 36.5625 | 77 | py |
ERD | ERD-main/configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-90k_coco.py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-270k_coco.py' # noqa
# training schedule for 90k
max_iters = 90000
# learning rate policy
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin... | 495 | 25.105263 | 93 | py |
ERD | ERD-main/configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-90k_coco.py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-270k_coco.py' # noqa
# training schedule for 90k
max_iters = 90000
# learning rate policy
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, ... | 491 | 24.894737 | 89 | py |
ERD | ERD-main/configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-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'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='Sy... | 1,204 | 36.65625 | 77 | py |
ERD | ERD-main/configs/vfnet/vfnet_r50_fpn_ms-2x_coco.py | _base_ = './vfnet_r50_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', prob=0.5),
... | 1,182 | 30.972973 | 78 | py |
ERD | ERD-main/configs/vfnet/vfnet_r101-mdconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-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',
... | 541 | 32.875 | 74 | py |
ERD | ERD-main/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',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 3,154 | 29.047619 | 79 | py |
ERD | ERD-main/configs/vfnet/vfnet_x101-32x4d-mdconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-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_e... | 580 | 31.277778 | 76 | py |
ERD | ERD-main/configs/vfnet/vfnet_x101-64x4d_fpn_ms-2x_coco.py | _base_ = './vfnet_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 | 26.6875 | 76 | py |
ERD | ERD-main/configs/vfnet/vfnet_res2net-101_fpn_ms-2x_coco.py | _base_ = './vfnet_r50_fpn_ms-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,
... | 459 | 26.058824 | 62 | py |
ERD | ERD-main/configs/vfnet/vfnet_x101-64x4d-mdconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './vfnet_r50-mdconv-c3-c5_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_e... | 580 | 31.277778 | 76 | py |
ERD | ERD-main/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 |
ERD | ERD-main/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')))
# learning policy
max_epochs = 24
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=... | 519 | 23.761905 | 78 | py |
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