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/cascade_rcnn/cascade-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 216 | 26.125 | 61 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_ms-3x_coco.py | _base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
| 105 | 20.2 | 51 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_ms-3x_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 208 | 28.857143 | 61 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_ms-3x_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_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='... | 430 | 27.733333 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
| 183 | 29.666667 | 73 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './cascade-mask-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='pyt... | 427 | 27.533333 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_ms-3x_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_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='... | 430 | 27.733333 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_1x_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 205 | 28.428571 | 61 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py | _base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires... | 502 | 25.473684 | 66 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './cascade-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'... | 422 | 27.2 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_20e_coco.py | _base_ = './cascade-mask-rcnn_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),
style='py... | 428 | 27.6 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
type='CascadeRCNN',
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),... | 446 | 26.9375 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x8d_fpn_ms-3x_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
... | 758 | 29.36 | 68 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
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(req... | 483 | 27.470588 | 66 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py | _base_ = './cascade-rcnn_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/nas_fcos/nas-fcos_r50-caffe_fpn_fcoshead-gn-head_4xb4-1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='NASFCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
... | 2,179 | 27.684211 | 73 | py |
ERD | ERD-main/configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='NASFCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
... | 2,157 | 27.773333 | 73 | py |
ERD | ERD-main/configs/rpn/rpn_r50-caffe_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
# use caffe img_norm
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(requires_grad=Fal... | 493 | 28.058824 | 66 | py |
ERD | ERD-main/configs/rpn/rpn_x101-64x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 413 | 26.6 | 76 | py |
ERD | ERD-main/configs/rpn/rpn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
# inference on val dataset and dump the proposals with evaluate metric
# data... | 1,169 | 30.621622 | 78 | py |
ERD | ERD-main/configs/rpn/rpn_x101-64x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_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),
style='pytorch',
... | 413 | 26.6 | 76 | py |
ERD | ERD-main/configs/rpn/rpn_x101-32x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 413 | 26.6 | 76 | py |
ERD | ERD-main/configs/rpn/rpn_r101_fpn_1x_coco.py | _base_ = './rpn_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/rpn/rpn_r101-caffe_fpn_1x_coco.py | _base_ = './rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 216 | 26.125 | 67 | py |
ERD | ERD-main/configs/rpn/rpn_r50_fpn_2x_coco.py | _base_ = './rpn_r50_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',
begi... | 422 | 22.5 | 79 | py |
ERD | ERD-main/configs/rpn/rpn_x101-32x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_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),
style='pytorch',
... | 413 | 26.6 | 76 | py |
ERD | ERD-main/configs/rpn/rpn_r50-caffe-c4_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
| 251 | 27 | 72 | py |
ERD | ERD-main/configs/rpn/rpn_r101_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_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/deformable_detr/deformable-detr-refine_r50_16xb2-50e_coco.py | _base_ = 'deformable-detr_r50_16xb2-50e_coco.py'
model = dict(with_box_refine=True)
| 84 | 27.333333 | 48 | py |
ERD | ERD-main/configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DeformableDETR',
num_queries=300,
num_feature_levels=4,
with_box_refine=False,
as_two_stage=False,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28... | 5,467 | 33.828025 | 79 | py |
ERD | ERD-main/configs/deformable_detr/deformable-detr-refine-twostage_r50_16xb2-50e_coco.py | _base_ = 'deformable-detr-refine_r50_16xb2-50e_coco.py'
model = dict(as_two_stage=True)
| 88 | 28.666667 | 55 | py |
ERD | ERD-main/configs/boxinst/boxinst_r50_fpn_ms-90k_coco.py | _base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='BoxInst',
data_preprocessor=dict(
type='BoxInstDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
mask_stride=4,
pa... | 2,693 | 27.659574 | 78 | py |
ERD | ERD-main/configs/boxinst/boxinst_r101_fpn_ms-90k_coco.py | _base_ = './boxinst_r50_fpn_ms-90k_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 217 | 23.222222 | 61 | py |
ERD | ERD-main/configs/res2net/htc_res2net-101_fpn_20e_coco.py | _base_ = '../htc/htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 276 | 24.181818 | 62 | py |
ERD | ERD-main/configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 291 | 25.545455 | 62 | py |
ERD | ERD-main/configs/res2net/mask-rcnn_res2net-101_fpn_2x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 287 | 25.181818 | 62 | py |
ERD | ERD-main/configs/res2net/cascade-rcnn_res2net-101_fpn_20e_coco.py | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 294 | 25.818182 | 62 | py |
ERD | ERD-main/configs/res2net/cascade-mask-rcnn_res2net-101_fpn_20e_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 299 | 26.272727 | 64 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-2x_lvis-v0.5.py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 216 | 30 | 61 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-1x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.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),
s... | 436 | 28.133333 | 76 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v0.5_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)),
test_cfg=dict(
rcnn... | 424 | 29.357143 | 76 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-1x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.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),
s... | 436 | 28.133333 | 76 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.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),
... | 438 | 28.266667 | 76 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.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),
... | 438 | 28.266667 | 76 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-1x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 214 | 29.714286 | 61 | py |
ERD | ERD-main/configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=d... | 422 | 29.214286 | 76 | py |
ERD | ERD-main/configs/yolof/yolof_r50-c5_8xb8-1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=Fals... | 3,591 | 29.700855 | 77 | py |
ERD | ERD-main/configs/yolof/yolof_r50-c5_8xb8-iter-1x_coco.py | _base_ = './yolof_r50-c5_8xb8-1x_coco.py'
# We implemented the iter-based config according to the source code.
# COCO dataset has 117266 images after filtering. We use 8 gpu and
# 8 batch size training, so 22500 is equivalent to
# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch,
# 20000 is equivalent... | 1,030 | 30.242424 | 79 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r101-gcb-r4-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 257 | 27.666667 | 56 | py |
ERD | ERD-main/configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5_fpn_1x_coco.py | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 183 | 35.8 | 75 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
... | 369 | 29.833333 | 60 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... | 369 | 29.833333 | 61 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r101-syncbn_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 163 | 31.8 | 75 | py |
ERD | ERD-main/configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r16-gcb-c3-c5_fpn_1x_coco.py | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, T... | 390 | 31.583333 | 73 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r50-syncbn_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 162 | 31.6 | 75 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True... | 375 | 30.333333 | 60 | py |
ERD | ERD-main/configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 180 | 35.2 | 75 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
... | 368 | 29.75 | 60 | py |
ERD | ERD-main/configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r16-gcb-c3-c5_fpn_1x_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True... | 387 | 31.333333 | 70 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 169 | 33 | 75 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, Tru... | 376 | 30.416667 | 61 | py |
ERD | ERD-main/configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r4-gcb-c3-c5_fpn_1x_coco.py | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, Tr... | 389 | 31.5 | 73 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r50-gcb-r16-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 257 | 27.666667 | 57 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r101-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... | 370 | 29.916667 | 61 | py |
ERD | ERD-main/configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r4-gcb-c3-c5_fpn_1x_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True,... | 386 | 31.25 | 70 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r101-gcb-r16-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 258 | 27.777778 | 57 | py |
ERD | ERD-main/configs/gcnet/mask-rcnn_r50-gcb-r4-c3-c5_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 256 | 27.555556 | 56 | py |
ERD | ERD-main/configs/instaboost/mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py | _base_ = './mask-rcnn_r50_fpn_instaboost-4x_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='... | 430 | 27.733333 | 76 | py |
ERD | ERD-main/configs/instaboost/cascade-mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_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),
... | 438 | 28.266667 | 76 | py |
ERD | ERD-main/configs/instaboost/cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
d... | 1,106 | 26 | 79 | py |
ERD | ERD-main/configs/instaboost/mask-rcnn_r101_fpn_instaboost-4x_coco.py | _base_ = './mask-rcnn_r50_fpn_instaboost-4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 208 | 28.857143 | 61 | py |
ERD | ERD-main/configs/instaboost/cascade-mask-rcnn_r101_fpn_instaboost-4x_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 217 | 26.25 | 61 | py |
ERD | ERD-main/configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
dx=15,
... | 1,095 | 25.731707 | 79 | py |
ERD | ERD-main/configs/detr/detr_r18_8xb2-500e_coco.py | _base_ = './detr_r50_8xb2-500e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[512]))
| 206 | 24.875 | 79 | py |
ERD | ERD-main/configs/detr/detr_r50_8xb2-500e_coco.py | _base_ = './detr_r50_8xb2-150e_coco.py'
# learning policy
max_epochs = 500
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=10)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[334],
... | 613 | 23.56 | 71 | py |
ERD | ERD-main/configs/detr/detr_r50_8xb2-150e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DETR',
num_queries=100,
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... | 5,433 | 33.833333 | 79 | py |
ERD | ERD-main/configs/detr/detr_r101_8xb2-500e_coco.py | _base_ = './detr_r50_8xb2-500e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 196 | 23.625 | 61 | py |
ERD | ERD-main/configs/atss/atss_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './atss_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]))
| 232 | 28.125 | 79 | py |
ERD | ERD-main/configs/atss/atss_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='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 2,536 | 29.939024 | 79 | py |
ERD | ERD-main/configs/atss/atss_r101_fpn_1x_coco.py | _base_ = './atss_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/atss/atss_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='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 2,164 | 29.069444 | 79 | py |
ERD | ERD-main/configs/atss/atss_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './atss_r50_fpn_8xb8-amp-lsj-200e_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/ld/ld_r34-gflv1-r101_fpn_1x_coco.py | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
model = dict(
backbone=dict(
type='ResNet',
depth=34,
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',
init_c... | 569 | 27.5 | 79 | py |
ERD | ERD-main/configs/ld/ld_r50-gflv1-r101_fpn_1x_coco.py | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_c... | 572 | 27.65 | 79 | py |
ERD | ERD-main/configs/ld/ld_r18-gflv1-r101_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa
model = dict(
type='Kn... | 2,361 | 32.267606 | 163 | py |
ERD | ERD-main/configs/ld/ld_r101-gflv1-r101-dcn_fpn_2x_coco.py | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa
model = dict(
teacher_config='configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py... | 1,608 | 31.18 | 187 | py |
ERD | ERD-main/configs/yolo/yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py | _base_ = ['../_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=32)
model = dict(
type='YOLOV3',
data_preproce... | 5,645 | 30.898305 | 79 | py |
ERD | ERD-main/configs/yolo/yolov3_d53_8xb8-320-273e_coco.py | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
input_size = (320, 320)
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
# `mean` and `to_rgb` should be the same with the `preprocess_cfg`
dict(type='Expand', mean=[0,... | 1,157 | 37.6 | 73 | py |
ERD | ERD-main/configs/yolo/yolov3_d53_8xb8-amp-ms-608-273e_coco.py | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
| 132 | 32.25 | 66 | py |
ERD | ERD-main/configs/yolo/yolov3_d53_8xb8-ms-608-273e_coco.py | _base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
# model settings
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[0, 0, 0],
std=[255., 255., 255.],
bgr_to_rgb=True,
pad_size_divisor=32)
model = dict(
type='YOLOV3',
data_preprocessor=data_preprocesso... | 5,442 | 31.39881 | 79 | py |
ERD | ERD-main/configs/yolo/yolov3_mobilenetv2_8xb24-320-300e_coco.py | _base_ = ['./yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
... | 1,505 | 34.023256 | 76 | py |
ERD | ERD-main/configs/yolo/yolov3_d53_8xb8-ms-416-273e_coco.py | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
# `mean` and `to_rgb` should be the same with the `preprocess_cfg`
dict(type='Expand', mean=[0, 0, 0], to_rgb=True, rat... | 1,153 | 38.793103 | 79 | py |
ERD | ERD-main/configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvi... | 3,534 | 35.822917 | 79 | py |
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 222 | 30.857143 | 66 | py |
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 199 | 32.333333 | 70 | py |
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.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(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', tem... | 1,811 | 29.2 | 73 | py |
ERD | ERD-main/configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | 4,108 | 34.119658 | 79 | py |
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 248 | 34.571429 | 92 | py |
ERD | ERD-main/configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py | _base_ = './cascade-mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 218 | 35.5 | 89 | py |
ERD | ERD-main/configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', ... | 1,237 | 30.74359 | 76 | py |
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