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/gfl/gfl_x101-32x4d_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
type='GFL',
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_... | 456 | 25.882353 | 76 | py |
ERD | ERD-main/configs/gfl/gfl_r50_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_1x_coco.py'
max_epochs = 24
# 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=[16, 22],
... | 774 | 25.724138 | 79 | py |
ERD | ERD-main/configs/gfl/gfl_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=... | 1,986 | 28.656716 | 79 | py |
ERD | ERD-main/configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './gfl_r50_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),
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False)... | 524 | 31.8125 | 72 | py |
ERD | ERD-main/configs/tridentnet/tridentnet_r50-caffe_1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='TridentFasterRCNN',
backbone=dict(
type='TridentResNet',
trident_dilations=(1, 2, 3),
... | 748 | 31.565217 | 74 | py |
ERD | ERD-main/configs/tridentnet/tridentnet_r50-caffe_ms-1x_coco.py | _base_ = 'tridentnet_r50-caffe_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(133... | 505 | 30.625 | 73 | py |
ERD | ERD-main/configs/tridentnet/tridentnet_r50-caffe_ms-3x_coco.py | _base_ = 'tridentnet_r50-caffe_ms-1x_coco.py'
# learning rate
max_epochs = 36
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',
... | 431 | 21.736842 | 79 | py |
ERD | ERD-main/configs/ssd/ssd512_coco.py | _base_ = 'ssd300_coco.py'
# model settings
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 2... | 2,132 | 33.967213 | 76 | py |
ERD | ERD-main/configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
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=1)
... | 5,043 | 30.72327 | 79 | py |
ERD | ERD-main/configs/ssd/ssd300_coco.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations... | 2,414 | 32.541667 | 74 | py |
ERD | ERD-main/configs/dcnv2/faster-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_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)))
| 216 | 35.166667 | 74 | py |
ERD | ERD-main/configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_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)))
| 212 | 34.5 | 74 | py |
ERD | ERD-main/configs/dcnv2/faster-rcnn_r50-mdconv-group4-c3-c5_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 74 | py |
ERD | ERD-main/configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='ModulatedDeformRoIPoolPack',
output_size=7,
o... | 417 | 31.153846 | 56 | py |
ERD | ERD-main/configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_amp-1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_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)))
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = di... | 393 | 34.818182 | 74 | py |
ERD | ERD-main/configs/nas_fpn/retinanet_r50_fpn_crop640-50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.6... | 2,521 | 30.924051 | 79 | py |
ERD | ERD-main/configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py | _base_ = './retinanet_r50_fpn_crop640-50e_coco.py'
# model settings
model = dict(
# `pad_size_divisor=128` ensures the feature maps sizes
# in `NAS_FPN` won't mismatch.
data_preprocessor=dict(pad_size_divisor=128),
neck=dict(
_delete_=True,
type='NASFPN',
in_channels=[256, 512, ... | 480 | 27.294118 | 59 | py |
ERD | ERD-main/configs/rtmdet/rtmdet_s_8xb32-300e_coco.py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='bac... | 2,096 | 32.285714 | 126 | py |
ERD | ERD-main/configs/rtmdet/rtmdet-ins_l_8xb32-300e_coco.py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='RTMDetInsSepBNHead',
num_classes=80,
in_channels=256,
stacked_convs=2,
share_conv=True,
pred_kernel_size=1,
feat_channels=256,
act_cfg=dict(type='SiLU',... | 3,140 | 28.914286 | 79 | py |
ERD | ERD-main/configs/rtmdet/rtmdet_m_8xb32-300e_coco.py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
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))
| 250 | 34.857143 | 79 | py |
ERD | ERD-main/configs/rtmdet/rtmdet-ins_s_8xb32-300e_coco.py | _base_ = './rtmdet-ins_l_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix=... | 2,492 | 29.777778 | 126 | py |
ERD | ERD-main/configs/rtmdet/rtmdet_l_8xb32-300e_coco.py | _base_ = [
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py',
'../_base_/datasets/coco_detection.py', './rtmdet_tta.py'
]
model = dict(
type='RTMDet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395... | 5,222 | 28.178771 | 73 | py |
ERD | ERD-main/configs/rtmdet/rtmdet-ins_x_8xb16-300e_coco.py | _base_ = './rtmdet-ins_l_8xb32-300e_coco.py'
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))
base_lr = 0.002
# optimizer
optim_wrapper = dict(optim... | 795 | 23.875 | 74 | py |
ERD | ERD-main/configs/rtmdet/rtmdet_tta.py | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... | 1,170 | 31.527778 | 78 | py |
ERD | ERD-main/configs/rtmdet/rtmdet_tiny_8xb32-300e_coco.py | _base_ = './rtmdet_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained', pre... | 1,435 | 31.636364 | 129 | py |
ERD | ERD-main/configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py | _base_ = './rtmdet-ins_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained',... | 1,546 | 30.571429 | 129 | py |
ERD | ERD-main/configs/rtmdet/rtmdet_x_8xb32-300e_coco.py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
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))
| 260 | 31.625 | 74 | py |
ERD | ERD-main/configs/rtmdet/rtmdet-ins_m_8xb32-300e_coco.py | _base_ = './rtmdet-ins_l_8xb32-300e_coco.py'
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))
| 254 | 35.428571 | 79 | py |
ERD | ERD-main/configs/rtmdet/classification/cspnext-m_8xb256-rsb-a1-600e_in1k.py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=0.67, widen_factor=0.75),
head=dict(in_channels=768))
| 155 | 25 | 57 | py |
ERD | ERD-main/configs/rtmdet/classification/cspnext-tiny_8xb256-rsb-a1-600e_in1k.py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=0.167, widen_factor=0.375),
head=dict(in_channels=384))
| 157 | 25.333333 | 59 | py |
ERD | ERD-main/configs/rtmdet/classification/cspnext-s_8xb256-rsb-a1-600e_in1k.py | _base_ = [
'mmcls::_base_/datasets/imagenet_bs256_rsb_a12.py',
'mmcls::_base_/schedules/imagenet_bs2048_rsb.py',
'mmcls::_base_/default_runtime.py'
]
model = dict(
type='ImageClassifier',
backbone=dict(
type='mmdet.CSPNeXt',
arch='P5',
out_indices=(4, ),
expand_ratio... | 1,629 | 24.076923 | 76 | py |
ERD | ERD-main/configs/rtmdet/classification/cspnext-l_8xb256-rsb-a1-600e_in1k.py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=1, widen_factor=1),
head=dict(in_channels=1024))
| 150 | 24.166667 | 51 | py |
ERD | ERD-main/configs/rtmdet/classification/cspnext-x_8xb256-rsb-a1-600e_in1k.py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=1.33, widen_factor=1.25),
head=dict(in_channels=1280))
| 156 | 25.166667 | 57 | py |
ERD | ERD-main/configs/paa/paa_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='PAA',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 2,384 | 28.444444 | 79 | py |
ERD | ERD-main/configs/paa/paa_r101_fpn_ms-3x_coco.py | _base_ = './paa_r50_fpn_ms-3x_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/paa/paa_r50_fpn_2x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
max_epochs = 24
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
ga... | 399 | 20.052632 | 79 | py |
ERD | ERD-main/configs/paa/paa_r101_fpn_1x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191 | 26.428571 | 61 | py |
ERD | ERD-main/configs/paa/paa_r50_fpn_1.5x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
max_epochs = 18
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[12, 16],
ga... | 401 | 20.157895 | 79 | py |
ERD | ERD-main/configs/paa/paa_r50_fpn_ms-3x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
max_epochs = 36
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[28, 34],
ga... | 777 | 24.933333 | 79 | py |
ERD | ERD-main/configs/paa/paa_r101_fpn_2x_coco.py | _base_ = './paa_r101_fpn_1x_coco.py'
max_epochs = 24
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
g... | 400 | 20.105263 | 79 | py |
ERD | ERD-main/configs/yolact/yolact_r50_1xb8-55e_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py', '../_base_/default_runtime.py'
]
img_norm_cfg = dict(
mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True)
# model settings
input_size = 550
model = dict(
type='YOLACT',
data_preprocessor=dict(
type='DetDataPreprocessor',
... | 5,373 | 30.426901 | 79 | py |
ERD | ERD-main/configs/yolact/yolact_r50_8xb8-55e_coco.py | _base_ = 'yolact_r50_1xb8-55e_coco.py'
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(lr=8e-3),
clip_grad=dict(max_norm=35, norm_type=2))
# learning rate
max_epochs = 55
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000),
dict(
... | 652 | 26.208333 | 79 | py |
ERD | ERD-main/configs/yolact/yolact_r101_1xb8-55e_coco.py | _base_ = './yolact_r50_1xb8-55e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 23.75 | 61 | py |
ERD | ERD-main/configs/cornernet/cornernet_hourglass104_8xb6-210e-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... | 5,555 | 29.195652 | 79 | py |
ERD | ERD-main/configs/cornernet/cornernet_hourglass104_10xb5-crop511-210e-mstest_coco.py | _base_ = './cornernet_hourglass104_8xb6-210e-mstest_coco.py'
train_dataloader = dict(batch_size=5)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (10 GPUs) x (5 samples per GPU)
auto_scale_lr = dict(base_batch_size=50)
| 288 | 31.111111 | 60 | py |
ERD | ERD-main/configs/cornernet/cornernet_hourglass104_32xb3-210e-mstest_coco.py | _base_ = './cornernet_hourglass104_8xb6-210e-mstest_coco.py'
train_dataloader = dict(batch_size=3)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (3 samples per GPU)
auto_scale_lr = dict(base_batch_size=96)
| 288 | 31.111111 | 60 | py |
ERD | ERD-main/configs/resnet_strikes_back/cascade-mask-rcnn_r50-rsb-pre_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
m... | 620 | 37.8125 | 135 | py |
ERD | ERD-main/configs/resnet_strikes_back/retinanet_r50-rsb-pre_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'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = ... | 613 | 37.375 | 135 | py |
ERD | ERD-main/configs/resnet_strikes_back/faster-rcnn_r50-rsb-pre_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'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model ... | 615 | 37.5 | 135 | py |
ERD | ERD-main/configs/resnet_strikes_back/mask-rcnn_r50-rsb-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'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = d... | 612 | 37.3125 | 135 | py |
ERD | ERD-main/configs/crowddet/crowddet-rcnn_refine_r50_fpn_8xb2-30e_crowdhuman.py | _base_ = './crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py'
model = dict(roi_head=dict(bbox_head=dict(with_refine=True)))
| 121 | 29.5 | 61 | py |
ERD | ERD-main/configs/crowddet/crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py | _base_ = ['../_base_/default_runtime.py']
model = dict(
type='CrowdDet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False,
pad_size_divisor=64,
# This option is set according to http... | 7,480 | 31.811404 | 79 | py |
ERD | ERD-main/configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco.py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=Tru... | 2,968 | 28.39604 | 78 | py |
ERD | ERD-main/configs/mask2former/mask2former_r101_8xb2-lsj-50e_coco.py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco.py']
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 208 | 25.125 | 61 | py |
ERD | ERD-main/configs/mask2former/mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py | _base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... | 1,471 | 37.736842 | 124 | py |
ERD | ERD-main/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_8xb2-lsj-50e_coco-panoptic.py | _base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| 295 | 48.333333 | 128 | py |
ERD | ERD-main/configs/mask2former/mask2former_r101_8xb2-lsj-50e_coco-panoptic.py | _base_ = './mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 215 | 26 | 61 | py |
ERD | ERD-main/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_16xb1-lsj-100e_coco-panoptic.py | _base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(
embed_dims=192,
num_heads=[6, 12, 24, 48],
init_cfg=dict(... | 999 | 37.461538 | 129 | py |
ERD | ERD-main/configs/mask2former/mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
... | 1,978 | 32.542373 | 123 | py |
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/mask2former/mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco.py | _base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... | 1,462 | 37.5 | 124 | py |
ERD | ERD-main/configs/mask2former/mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py | _base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
d... | 1,614 | 36.55814 | 124 | py |
ERD | ERD-main/configs/mask2former/mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco.py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
em... | 1,967 | 33.526316 | 123 | 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/htc_r50-sac_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)))
| 245 | 26.333333 | 50 | 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/cascade-rcnn_r50-sac_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... | 382 | 28.461538 | 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/carafe/mask-rcnn_r50_fpn-carafe_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_size_divisor=64),
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
... | 887 | 27.645161 | 52 | py |
ERD | ERD-main/configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_size_divisor=64),
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
... | 584 | 26.857143 | 56 | py |
ERD | ERD-main/configs/common/ms-poly-90k_coco-instance.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/d... | 3,896 | 28.748092 | 78 | py |
ERD | ERD-main/configs/common/lsj-100e_coco-detection.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# dat... | 3,769 | 29.650407 | 79 | py |
ERD | ERD-main/configs/common/ms_3x_coco.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/... | 3,449 | 30.651376 | 79 | py |
ERD | ERD-main/configs/common/lsj-200e_coco-detection.py | _base_ = './lsj-100e_coco-detection.py'
# 8x25=200e
train_dataloader = dict(dataset=dict(times=8))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=25,
by_ep... | 380 | 19.052632 | 69 | py |
ERD | ERD-main/configs/common/lsj-200e_coco-instance.py | _base_ = './lsj-100e_coco-instance.py'
# 8x25=200e
train_dataloader = dict(dataset=dict(times=8))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=25,
by_epo... | 379 | 19 | 69 | py |
ERD | ERD-main/configs/common/lsj-100e_coco-instance.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# dat... | 3,811 | 29.99187 | 79 | py |
ERD | ERD-main/configs/common/ssj_270k_coco-instance.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# da... | 3,943 | 30.301587 | 78 | py |
ERD | ERD-main/configs/common/ssj_scp_270k_coco-instance.py | _base_ = 'ssj_270k_coco-instance.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_... | 1,949 | 30.967213 | 78 | py |
ERD | ERD-main/configs/common/ms_3x_coco-instance.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/... | 3,481 | 30.944954 | 79 | py |
ERD | ERD-main/configs/common/ms-poly_3x_coco-instance.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/d... | 3,680 | 29.932773 | 79 | py |
ERD | ERD-main/configs/common/ms-90k_coco.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/d... | 3,754 | 29.528455 | 78 | py |
ERD | ERD-main/configs/timm_example/retinanet_timm-efficientnet-b1_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in m... | 725 | 29.25 | 75 | py |
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