repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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ERD | ERD-main/configs/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_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/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/instances_train2017.1@2.json'
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/instances_train2017.1@5.json'
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/instances_train2017.1@1.json'
unlabeled_dataset.ann_file = 'semi_anns/insta... | 445 | 43.6 | 79 | 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/libra_rcnn/libra-faster-rcnn_r101_fpn_1x_coco.py | _base_ = './libra-faster-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/libra_rcnn/libra-faster-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './libra-faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pyt... | 427 | 27.533333 | 76 | py |
ERD | ERD-main/configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py | # We follow the original implementation which
# adopts the Caffe pre-trained backbone.
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='AutoAssign',
data_preprocessor=dict(
type='DetData... | 1,923 | 26.485714 | 72 | py |
ERD | ERD-main/configs/gfl_increment/gfl_r50_fpn_1x_coco_first_40_incre_last_40_cats.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = '../data/coco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotation... | 3,820 | 31.65812 | 129 | py |
ERD | ERD-main/configs/gfl_increment/gfl_r50_fpn_1x_coco_first_40_cats.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = '../data/coco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotation... | 3,407 | 29.702703 | 80 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_ms-3x_coco.py | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=36)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=36,
... | 389 | 20.666667 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_ms-2x_coco.py | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
... | 388 | 21.882353 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101_fpn_ms-640-800-3x_coco.py | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9,... | 343 | 33.4 | 77 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_ms-1x_coco.py | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
(1333, 800)],
keep... | 470 | 28.4375 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 213 | 25.75 | 61 | py |
ERD | ERD-main/configs/retinanet/retinanet_x101-64x4d_fpn_2x_coco.py | _base_ = './retinanet_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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/configs/retinanet/retinanet_r18_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
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
n... | 682 | 31.52381 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101-caffe_fpn_1x_coco.py | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 222 | 26.875 | 67 | py |
ERD | ERD-main/configs/retinanet/retinanet_x101-64x4d_fpn_1x_coco.py | _base_ = './retinanet_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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
ERD | ERD-main/configs/retinanet/retinanet_x101-32x4d_fpn_2x_coco.py | _base_ = './retinanet_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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101-caffe_fpn_ms-3x_coco.py | _base_ = './retinanet_r50-caffe_fpn_ms-3x_coco.py'
# learning policy
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 243 | 26.111111 | 67 | py |
ERD | ERD-main/configs/retinanet/retinanet_x101-32x4d_fpn_1x_coco.py | _base_ = './retinanet_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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/configs/retinanet/retinanet_r18_fpn_1xb8-1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# data
train_dataloader = dict(batch_size=8)
# model
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained'... | 797 | 30.92 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './retinanet_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]))
| 237 | 28.75 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
# use caffe img_norm
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... | 507 | 28.882353 | 66 | py |
ERD | ERD-main/configs/free_anchor/freeanchor_x101-32x4d_fpn_1x_coco.py | _base_ = './freeanchor_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,
style='pytorch',
init_cfg=dict(
type='Pretrained', ... | 366 | 25.214286 | 76 | py |
ERD | ERD-main/configs/free_anchor/freeanchor_r101_fpn_1x_coco.py | _base_ = './freeanchor_r50_fpn_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/faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './faster-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',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.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_... | 457 | 29.533333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './faster-rcnn_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]))
| 239 | 29 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 24.25 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.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_... | 457 | 29.533333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py | _base_ = 'faster-rcnn_r50-caffe-dc5_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
... | 498 | 32.266667 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person.py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
metainfo = {
'classes': ('person', ),
'palette': [
(220, 20, 60),
]
}
train_dataloader = dict(dataset=dict(metainfo=metainfo))
val_dataloader = dict(dataset=dict(metainfo=metainfo))
test_... | 582 | 37.866667 | 209 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.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=[57.3... | 784 | 31.708333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 215 | 26 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... | 480 | 29.0625 | 66 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_1x_coco.py | _base_ = './faster-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 224 | 27.125 | 67 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-90k_coco.py | _base_ = 'faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
max_iter = 90000
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[60000, 80000],
... | 561 | 22.416667 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 183 | 29.666667 | 72 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_2x_coco.py | _base_ = './faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 311 | 25 | 67 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_fcos-rpn_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(
# copied from configs/fcos/fcos_r50-caffe_fpn_gn-head_1x_coco.py
neck=dict(
start_level=1,
add_extra_con... | 1,520 | 30.040816 | 73 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-c4_ms-1x_coco.py | _base_ = './faster-rcnn_r50-caffe_c4-1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
... | 499 | 32.333333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-2x_coco.py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# param_scheduler = [
# dict(
# type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa
# dict(
# type='MultiStepLR',
# begi... | 505 | 25.631579 | 88 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_c4-1x_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'
]
| 182 | 29.5 | 72 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... | 1,082 | 32.84375 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-3x_coco.py | _base_ = './faster-rcnn_r50-caffe-dc5_ms-1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# param_scheduler = [
# dict(
# type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa
# dict(
# type='MultiStepLR',
# begi... | 505 | 25.631579 | 88 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-tnr-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.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... | 569 | 37 | 77 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... | 481 | 29.125 | 66 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_2x_coco.py | _base_ = './faster-rcnn_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',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_2x_coco.py | _base_ = './faster-rcnn_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',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_90k_coco.py | _base_ = 'faster-rcnn_r50-caffe_fpn_1x_coco.py'
max_iter = 90000
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[60000, 80000],
... | 557 | 23.26087 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person-bicycle-car.py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
metainfo = {
'classes': ('person', 'bicycle', 'car'),
'palette': [
(220, 20, 60),
(119, 11, 32),
(0, 0, 142),
]
}
train_dataloader = dict(dataset=dict(metainfo=metainfo))
... | 642 | 36.823529 | 209 | py |
ERD | ERD-main/configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py | _base_ = [
'../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]
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,
pad_mask=True,
mask_pad_value=0,
pad_seg=True,
... | 7,430 | 33.24424 | 79 | py |
ERD | ERD-main/configs/sabl/sabl-cascade-rcnn_r101_fpn_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 settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint... | 3,296 | 35.230769 | 79 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101-gn_fpn_ms-640-800-2x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 2,270 | 31.913043 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-faster-rcnn_r101_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(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://re... | 1,369 | 34.128205 | 77 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101-gn_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 settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 1,874 | 31.327586 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101_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 settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='t... | 1,785 | 30.892857 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101-gn_fpn_ms-480-960-2x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 2,270 | 31.913043 | 75 | py |
ERD | ERD-main/configs/pascal_voc/faster-rcnn_r50-caffe-c4_ms-18k_voc0712.py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/schedules/schedule_1x.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_arg... | 2,857 | 31.850575 | 79 | py |
ERD | ERD-main/configs/queryinst/queryinst_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py | _base_ = './queryinst_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 228 | 27.625 | 71 | py |
ERD | ERD-main/configs/queryinst/queryinst_r101_fpn_ms-480-800-3x_coco.py | _base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 209 | 25.25 | 61 | py |
ERD | ERD-main/configs/queryinst/queryinst_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='QueryInst',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.... | 5,345 | 33.269231 | 79 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './mask-rcnn_r101_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',
... | 420 | 27.066667 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-1x_coco.py | _base_ = './mask-rcnn_r101_fpn_1x_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(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 1,212 | 28.585366 | 73 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_2x_coco.py | _base_ = './mask-rcnn_x101-32x4d_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='pyto... | 426 | 27.466667 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './mask-rcnn_x101-32x4d_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='pyto... | 426 | 27.466667 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x4d_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(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 480 | 24.315789 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 894 | 29.862069 | 73 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 213 | 25.75 | 61 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-2x_coco.py | _base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py'
train_cfg = dict(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=24,
by_epoch=True,
mil... | 359 | 21.5 | 79 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe-c4_1x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 179 | 29 | 72 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 942 | 28.46875 | 73 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_1x_coco.py | _base_ = './mask-rcnn_r101_fpn_1x_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(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 683 | 28.73913 | 68 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_poly-1x_coco_v1.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 1,019 | 30.875 | 78 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/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(
depth=101,
norm_c... | 519 | 25 | 67 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-64x4d_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(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 480 | 24.315789 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 414 | 28.642857 | 66 | py |
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