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/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/vfnet/vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(dcn_on_last_conv=True))
| 243 | 33.857143 | 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_tta.py | # This is different from the TTA of official CenterNet.
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
tta_pipeline = [
dict(type='LoadImageFromFile', to_float32=True, backend_args=None),
dict(
type='TestTimeAug',
transform... | 1,361 | 33.05 | 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/centernet/centernet_r18_8xb16-crop512-140e_coco.py | _base_ = './centernet_r18-dcnv2_8xb16-crop512-140e_coco.py'
model = dict(neck=dict(use_dcn=False))
| 100 | 24.25 | 59 | 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_r50_fpn_4xb4-2x_coco.py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
# learning policy
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=max_epochs,
by_epoch=True,
milestones=[16, 22],
... | 379 | 22.75 | 79 | 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_r50_fpn_gn-head-align_4xb4-2x_coco.py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
... | 572 | 26.285714 | 79 | py |
ERD | ERD-main/configs/foveabox/fovea_r50_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
... | 901 | 28.096774 | 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/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DoubleHeadRoIHead',
reg_roi_scale_factor=1.3,
bbox_head=dict(
_delete_=True,
type='DoubleConvFCBBoxHead',
num_convs=4,
num_fcs=2,
in_channel... | 845 | 34.25 | 77 | 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/faster-rcnn_regnetx-3.2GF_fpn_2x_coco.py | _base_ = './faster-rcnn_regnetx-3.2GF_fpn_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,
... | 391 | 22.058824 | 79 | 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/mask-rcnn_regnetx-3.2GF-mdconv-c3-c5_fpn_1x_coco.py | _base_ = 'mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
| 305 | 37.25 | 74 | 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/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DynamicRoIHead',
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_... | 1,051 | 35.275862 | 77 | py |
ERD | ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-swav-pre_fpn_ms-2x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 811 | 30.230769 | 76 | py |
ERD | ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-swav-pre_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(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 416 | 28.785714 | 76 | py |
ERD | ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-mocov2-pre_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(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 418 | 28.928571 | 78 | py |
ERD | ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-mocov2-pre_fpn_ms-2x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 813 | 30.307692 | 78 | py |
ERD | ERD-main/configs/wider_face/retinanet_r50_fpn_1x_widerface.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/wider_face.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(bbox_head=dict(num_classes=1))
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, ... | 342 | 30.181818 | 77 | py |
ERD | ERD-main/configs/wider_face/ssd300_8xb32-24e_widerface.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_2x.py'
]
model = dict(bbox_head=dict(num_classes=1))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations... | 2,087 | 31.123077 | 75 | py |
ERD | ERD-main/configs/resnest/mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = './mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 248 | 30.125 | 65 | 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/cascade-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = './cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 257 | 31.25 | 74 | 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/resnest/cascade-mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = './cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 256 | 31.125 | 73 | py |
ERD | ERD-main/configs/resnest/faster-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = './faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 256 | 31.125 | 73 | py |
ERD | ERD-main/configs/groie/mask-rcnn_r101_fpn_syncbn-r4-gcb_c3-c5-groie_1x_coco.py | _base_ = '../gcnet/mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_ch... | 1,543 | 32.565217 | 78 | py |
ERD | ERD-main/configs/groie/mask-rcnn_r50_fpn_syncbn-r4-gcb-c3-c5-groie_1x_coco.py | _base_ = '../gcnet/mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_cha... | 1,542 | 32.543478 | 78 | py |
ERD | ERD-main/configs/groie/grid-rcnn_r50_fpn_gn-head-groie_1x_coco.py | _base_ = '../grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=25... | 1,534 | 32.369565 | 78 | py |
ERD | ERD-main/configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... | 834 | 31.115385 | 77 | py |
ERD | ERD-main/configs/groie/mask-rcnn_r50_fpn_groie_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... | 1,526 | 32.195652 | 78 | py |
ERD | ERD-main/configs/albu_example/mask-rcnn_r50_fpn_albu-1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.... | 1,954 | 28.179104 | 76 | py |
ERD | ERD-main/configs/fsaf/fsaf_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... | 1,459 | 29.416667 | 77 | 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 |
ERD | ERD-main/configs/fsaf/fsaf_x101-64x4d_fpn_1x_coco.py | _base_ = './fsaf_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',
... | 414 | 26.666667 | 76 | py |
ERD | ERD-main/configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='GridRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_si... | 5,068 | 30.484472 | 79 | py |
ERD | ERD-main/configs/grid_rcnn/grid-rcnn_x101-64x4d_fpn_gn-head_2x_coco.py | _base_ = './grid-rcnn_x101-32x4d_fpn_gn-head_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,
style='pytorch',
init_cfg=dict(
type=... | 380 | 26.214286 | 76 | py |
ERD | ERD-main/configs/grid_rcnn/grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py | _base_ = './grid-rcnn_r50_fpn_gn-head_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,
style='pytorch',
init_cfg=dict(
type='Pretra... | 373 | 25.714286 | 76 | py |
ERD | ERD-main/configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py | _base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
# training schedule
max_epochs = 12
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.0001, by_epoch=False, begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
... | 414 | 19.75 | 70 | py |
ERD | ERD-main/configs/grid_rcnn/grid-rcnn_r101_fpn_gn-head_2x_coco.py | _base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 206 | 24.875 | 61 | py |
ERD | ERD-main/configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True)
# model settings
model = dict(
type='CornerNet',
data_preprocessor=data_pr... | 5,563 | 29.571429 | 79 | py |
ERD | ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.02-coco.py | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 2% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/[email protected]'
unlabeled_dataset.ann_file = 'semi_anns/insta... | 445 | 43.6 | 79 | py |
ERD | ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py',
'../_base_/datasets/semi_coco_detection.py'
]
detector = _base_.model
detector.data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
... | 2,511 | 28.552941 | 79 | py |
ERD | ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.05-coco.py | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 5% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/[email protected]'
unlabeled_dataset.ann_file = 'semi_anns/insta... | 445 | 43.6 | 79 | py |
ERD | ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.01-coco.py | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 1% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/[email protected]'
unlabeled_dataset.ann_file = 'semi_anns/insta... | 445 | 43.6 | 79 | py |
ERD | ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# Using 32 GPUS while training
optim_wrapper = dict(
type='OptimWrappe... | 941 | 25.166667 | 75 | py |
ERD | ERD-main/configs/openimages/ssd300_32xb8-36e_openimages.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
bbox_head=dict(
num_classes=601,
anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
# dataset settings
dataset_typ... | 3,014 | 32.876404 | 79 | py |
ERD | ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
train_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file=... | 1,712 | 41.825 | 78 | py |
ERD | ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages-challenge.py | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| 195 | 31.666667 | 78 | py |
ERD | ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages.py | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| 185 | 30 | 78 | py |
ERD | ERD-main/configs/openimages/retinanet_r50_fpn_32xb2-1x_openimages.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=601))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor... | 905 | 24.166667 | 75 | py |
ERD | ERD-main/configs/_base_/default_runtime.py | default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(typ... | 759 | 29.4 | 76 | py |
ERD | ERD-main/configs/_base_/models/rpn_r50-caffe-c4.py | # model settings
model = dict(
type='RPN',
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(
type='ResNet',
depth=50,
num_stages=3,
... | 1,980 | 29.476923 | 72 | py |
ERD | ERD-main/configs/_base_/models/retinanet_r50_fpn.py | # model settings
model = dict(
type='RetinaNet',
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='ResNet',
depth=50,
num... | 2,059 | 28.855072 | 79 | py |
ERD | ERD-main/configs/_base_/models/faster-rcnn_r50-caffe-c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
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(
... | 4,018 | 31.41129 | 78 | py |
ERD | ERD-main/configs/_base_/models/faster-rcnn_r50-caffe-dc5.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
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(
... | 3,670 | 31.776786 | 77 | py |
ERD | ERD-main/configs/_base_/models/faster-rcnn_r50_fpn.py | # model settings
model = dict(
type='FasterRCNN',
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='ResNet',
depth=50,
nu... | 3,828 | 32.295652 | 79 | py |
ERD | ERD-main/configs/_base_/models/mask-rcnn_r50_fpn.py | # model settings
model = dict(
type='MaskRCNN',
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),
backbone=dict(
type='ResNet',
... | 4,273 | 32.390625 | 79 | py |
ERD | ERD-main/configs/_base_/models/rpn_r50_fpn.py | # model settings
model = dict(
type='RPN',
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='ResNet',
depth=50,
num_stage... | 2,004 | 29.846154 | 79 | py |
ERD | ERD-main/configs/_base_/models/ssd300.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
bgr_to_rgb=True,
pad_size_divisor=1),
backbone=dict(
type='SSDVGG',
depth=16,... | 1,959 | 29.625 | 71 | py |
ERD | ERD-main/configs/_base_/models/mask-rcnn_r50-caffe-c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_mask=True,
pad_size_divisor=32),
ba... | 4,275 | 31.150376 | 78 | py |
ERD | ERD-main/configs/_base_/models/cascade-mask-rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
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),
backbone=dict(
type='ResNet',
... | 7,169 | 34.147059 | 79 | py |
ERD | ERD-main/configs/_base_/models/cascade-rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
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='ResNet',
depth=50,
n... | 6,521 | 34.064516 | 79 | py |
ERD | ERD-main/configs/_base_/models/fast-rcnn_r50_fpn.py | # model settings
model = dict(
type='FastRCNN',
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='ResNet',
depth=50,
num_... | 2,256 | 31.710145 | 79 | py |
ERD | ERD-main/configs/_base_/schedules/schedule_20e.py | # training schedule for 20e
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='M... | 816 | 27.172414 | 79 | py |
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