repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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ERD | ERD-main/configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py | _base_ = [
'../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=True,
mask_pad_value=0,
pad_seg=True,
seg_pad_value=2... | 8,206 | 31.56746 | 79 | py |
ERD | ERD-main/configs/point_rend/point-rend_r50-caffe_fpn_ms-3x_coco.py | _base_ = './point-rend_r50-caffe_fpn_ms-1x_coco.py'
max_epochs = 36
# learning policy
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=[... | 391 | 19.631579 | 79 | py |
ERD | ERD-main/configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(
_d... | 1,448 | 31.2 | 75 | py |
ERD | ERD-main/configs/detectors/detectors_cascade-rcnn_r50_1x_coco.py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 1,053 | 30.939394 | 72 | py |
ERD | ERD-main/configs/detectors/detectors_htc-r50_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_ste... | 916 | 30.62069 | 57 | py |
ERD | ERD-main/configs/detectors/htc_r50-rfp_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=dict(
type='RFP',
rfp_steps=2,
aspp_out_channels=64,
aspp_dilations=(1, 3, 6, 1),
rfp_backbone=dic... | 714 | 27.6 | 57 | py |
ERD | ERD-main/configs/detectors/cascade-rcnn_r50-rfp_1x_coco.py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=d... | 851 | 28.37931 | 72 | py |
ERD | ERD-main/configs/detectors/detectors_htc-r101_20e_coco.py | _base_ = '../htc/htc_r101_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_s... | 920 | 30.758621 | 57 | py |
ERD | ERD-main/configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py | _base_ = 'fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model setting
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(
init_cfg=dict(
... | 1,087 | 23.727273 | 66 | py |
ERD | ERD-main/configs/fcos/fcos_x101-64x4d_fpn_gn-head_ms-640-800-2x_coco.py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNeXt'... | 1,429 | 25.981132 | 78 | py |
ERD | ERD-main/configs/fcos/fcos_r50-caffe_fpn_gn-head_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[102.9801, 115.9465, 122.7717],
std=[1.0, 1.0, 1.0],
... | 2,093 | 26.552632 | 78 | py |
ERD | ERD-main/configs/fcos/fcos_r50-caffe_fpn_gn-head_ms-640-800-2x_coco.py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 800)],
keep_ratio=... | 814 | 25.290323 | 78 | py |
ERD | ERD-main/configs/fcos/fcos_r101-caffe_fpn_gn-head-1x_coco.py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
| 242 | 23.3 | 66 | py |
ERD | ERD-main/configs/fcos/fcos_r101-caffe_fpn_gn-head_ms-640-800-2x_coco.py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', back... | 1,005 | 24.794872 | 78 | py |
ERD | ERD-main/configs/fcos/fcos_r50-caffe_fpn_gn-head_4xb4-1x_coco.py | # TODO: Remove this config after benchmarking all related configs
_base_ = 'fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_dataloader = dict(batch_size=4, num_workers=4)
| 188 | 30.5 | 65 | py |
ERD | ERD-main/configs/fcos/fcos_r50-caffe_fpn_gn-head-center_1x_coco.py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
| 146 | 28.4 | 76 | py |
ERD | ERD-main/configs/fcos/fcos_r18_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py | _base_ = './fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py' # noqa
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 281 | 34.25 | 100 | py |
ERD | ERD-main/configs/fcos/fcos_r101_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py | _base_ = './fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py' # noqa
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 257 | 31.25 | 100 | py |
ERD | ERD-main/configs/fcos/fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py | _base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# model settings
model = dict(
type='FCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.1... | 2,377 | 30.289474 | 79 | py |
ERD | ERD-main/configs/fcos/fcos_r50-dcn-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py | _base_ = 'fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
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(
dcn=dict(type='DCNv2'... | 1,212 | 25.369565 | 74 | py |
ERD | ERD-main/configs/ddod/ddod_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='DDOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rg... | 2,223 | 29.465753 | 79 | py |
ERD | ERD-main/configs/condinst/condinst_r50_fpn_ms-poly-90k_coco_instance.py | _base_ = '../common/ms-poly-90k_coco-instance.py'
# model settings
model = dict(
type='CondInst',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=True,
pad_size_divisor=32)... | 2,492 | 27.988372 | 78 | py |
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_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/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-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/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_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/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_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-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/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-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_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 |
ERD | ERD-main/configs/vfnet/vfnet_r101_fpn_ms-2x_coco.py | _base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 196 | 27.142857 | 61 | py |
ERD | ERD-main/configs/vfnet/vfnet_x101-32x4d_fpn_ms-2x_coco.py | _base_ = './vfnet_r50_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_eval=True,
... | 442 | 26.6875 | 76 | py |
ERD | ERD-main/configs/vfnet/vfnet_res2net101-mdconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './vfnet_r50-mdconv-c3-c5_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_e... | 597 | 30.473684 | 74 | py |
ERD | ERD-main/configs/centernet/centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 220 | 26.625 | 63 | py |
ERD | ERD-main/configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./centernet_tta.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPrepro... | 4,299 | 30.386861 | 79 | py |
ERD | ERD-main/configs/centernet/centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 2,397 | 27.547619 | 79 | py |
ERD | ERD-main/configs/centernet/centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './centernet-update_r50_fpn_8xb8-amp-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]))
| 244 | 29.625 | 79 | py |
ERD | ERD-main/configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0,... | 3,029 | 27.584906 | 73 | py |
ERD | ERD-main/configs/foveabox/fovea_r101_fpn_4xb4-1x_coco.py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 198 | 27.428571 | 61 | py |
ERD | ERD-main/configs/foveabox/fovea_r101_fpn_4xb4-2x_coco.py | _base_ = './fovea_r50_fpn_4xb4-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 198 | 27.428571 | 61 | py |
ERD | ERD-main/configs/foveabox/fovea_r101_fpn_gn-head-align_4xb4-2x_coco.py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# lear... | 650 | 26.125 | 79 | py |
ERD | ERD-main/configs/foveabox/fovea_r50_fpn_4xb4-1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 1,836 | 29.616667 | 79 | py |
ERD | ERD-main/configs/foveabox/fovea_r101_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_... | 1,042 | 28.8 | 79 | py |
ERD | ERD-main/configs/regnet/faster-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 523 | 28.111111 | 73 | py |
ERD | ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py | _base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
... | 856 | 28.551724 | 73 | py |
ERD | ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-400MF_fpn_ms-3x_coco.py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 528 | 28.388889 | 73 | py |
ERD | ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-800MF_fpn_ms-3x_coco.py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 529 | 28.444444 | 73 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-400MF_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 739 | 26.407407 | 76 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-1.6GF_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 740 | 26.444444 | 76 | py |
ERD | ERD-main/configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py']
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 831 | 31 | 79 | py |
ERD | ERD-main/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 520 | 27.944444 | 73 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_ms-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'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... | 1,826 | 28.95082 | 79 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-12GF_fpn_1x_coco.py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict... | 520 | 27.944444 | 72 | py |
ERD | ERD-main/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 520 | 27.944444 | 73 | py |
ERD | ERD-main/configs/regnet/faster-rcnn_regnetx-4GF_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 524 | 28.166667 | 73 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-4GF_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 741 | 26.481481 | 76 | py |
ERD | ERD-main/configs/regnet/faster-rcnn_regnetx-800MF_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 523 | 28.111111 | 73 | py |
ERD | ERD-main/configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.... | 968 | 30.258065 | 76 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-6.4GF_fpn_1x_coco.py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 522 | 28.055556 | 73 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-4GF_fpn_1x_coco.py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 521 | 28 | 73 | py |
ERD | ERD-main/configs/regnet/faster-rcnn_regnetx-400MF_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 522 | 28.055556 | 73 | py |
ERD | ERD-main/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.67... | 1,012 | 30.65625 | 76 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... | 965 | 30.16129 | 76 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-8GF_fpn_1x_coco.py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 521 | 28 | 73 | py |
ERD | ERD-main/configs/regnet/mask-rcnn_regnetx-800MF_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 740 | 26.444444 | 76 | py |
ERD | ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-4GF_fpn_ms-3x_coco.py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 530 | 28.5 | 73 | py |
ERD | ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 529 | 28.444444 | 73 | py |
ERD | ERD-main/configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeS... | 1,214 | 29.375 | 79 | py |
ERD | ERD-main/configs/resnest/cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type... | 3,590 | 34.205882 | 79 | py |
ERD | ERD-main/configs/resnest/cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResN... | 3,394 | 35.117021 | 79 | py |
ERD | ERD-main/configs/resnest/mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
... | 1,402 | 28.851064 | 79 | py |
ERD | ERD-main/configs/fsaf/fsaf_r101_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 192 | 26.571429 | 61 | py |
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