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
value |
|---|---|---|---|---|---|---|
GFocalV2 | GFocalV2-master/configs/gfocal/gfocal_r2n101_dcn_fpn_ms2x.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
pretrained='open-mmlab://res2net101_v1d_26w_4s',
backbone=dict(
type='Res2Net',
depth=101,
num_stages=4,
scales=4,
... | 3,816 | 29.293651 | 77 | py |
GFocalV2 | GFocalV2-master/configs/gfocal/gfocal_x101_dcn_fpn_ms2x.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
n... | 3,811 | 29.253968 | 77 | py |
GFocalV2 | GFocalV2-master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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', requ... | 363 | 25 | 53 | py |
GFocalV2 | GFocalV2-master/configs/fsaf/fsaf_r101_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 114 | 37.333333 | 76 | py |
GFocalV2 | GFocalV2-master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 128 | 31.25 | 76 | py |
GFocalV2 | GFocalV2-master/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(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='py... | 329 | 24.384615 | 56 | py |
GFocalV2 | GFocalV2-master/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(
pretrained='open-mmlab://resnext101_32x4d',
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')... | 610 | 24.458333 | 72 | py |
GFocalV2 | GFocalV2-master/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',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1... | 4,091 | 29.088235 | 78 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/retinanet_r50_fpn.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
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,
... | 1,657 | 26.180328 | 56 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/faster_rcnn_r50_fpn.py | model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
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'... | 3,415 | 29.5 | 77 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/cascade_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
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,
... | 6,001 | 31.619565 | 79 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/rpn_r50_caffe_c4.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type... | 1,655 | 27.067797 | 72 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
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,
... | 6,610 | 31.890547 | 79 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/fast_rcnn_r50_fpn.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
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,
... | 1,932 | 29.68254 | 77 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
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,
... | 3,858 | 29.872 | 78 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
out_indic... | 3,266 | 29.25 | 77 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/rpn_r50_fpn.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet50',
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... | 1,699 | 26.868852 | 72 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/ssd300.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 1,395 | 26.92 | 60 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/faster_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2,... | 3,481 | 28.760684 | 78 | py |
GFocalV2 | GFocalV2-master/configs/_base_/models/mask_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, )... | 3,840 | 29.007813 | 78 | py |
GFocalV2 | GFocalV2-master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 127 | 41.666667 | 76 | py |
GFocalV2 | GFocalV2-master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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(ty... | 376 | 25.928571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 150 | 29.2 | 57 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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',... | 368 | 25.357143 | 53 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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',... | 368 | 25.357143 | 53 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=Fals... | 1,473 | 33.27907 | 75 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
total_epochs = 24
| 124 | 24 | 55 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=Fals... | 1,329 | 34 | 75 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_r101_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
total_epochs = 36
| 124 | 24 | 55 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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',... | 368 | 25.357143 | 53 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_r101_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
GFocalV2 | GFocalV2-master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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',... | 368 | 25.357143 | 53 | py |
GFocalV2 | GFocalV2-master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytor... | 326 | 24.153846 | 53 | py |
GFocalV2 | GFocalV2-master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 131 | 43 | 76 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 152 | 29.6 | 57 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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... | 370 | 25.5 | 53 | py |
GFocalV2 | GFocalV2-master/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'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,304 | 33.342105 | 72 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=Fa... | 1,475 | 33.325581 | 75 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py | _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
total_epochs = 36
| 126 | 24.4 | 57 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 121 | 39.666667 | 76 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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... | 370 | 25.5 | 53 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
total_epochs = 24
| 126 | 24.4 | 57 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 121 | 39.666667 | 76 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=Fa... | 1,331 | 34.052632 | 75 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
total_epochs = 36
| 126 | 24.4 | 57 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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... | 370 | 25.5 | 53 | py |
GFocalV2 | GFocalV2-master/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'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,388 | 33.725 | 72 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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... | 370 | 25.5 | 53 | py |
GFocalV2 | GFocalV2-master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_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'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,448 | 32.697674 | 72 | py |
GFocalV2 | GFocalV2-master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_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(
pretrained='torchvision://resnet101',
backbo... | 2,362 | 31.819444 | 77 | py |
GFocalV2 | GFocalV2-master/configs/sabl/sabl_retinanet_r101_fpn_gn_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(
pretrained='torchvision://resnet101',
backbo... | 1,737 | 30.6 | 73 | py |
GFocalV2 | GFocalV2-master/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(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
roi_head=dict(bbox_head=[
d... | 3,227 | 35.269663 | 79 | py |
GFocalV2 | GFocalV2-master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_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(
pretrained='torchvision://resnet101',
backbo... | 2,362 | 31.819444 | 77 | py |
GFocalV2 | GFocalV2-master/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(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
roi_head=dict(
bbox_head=dict(
_d... | 1,300 | 34.162162 | 77 | py |
GFocalV2 | GFocalV2-master/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(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
bbox_head=dict(
_delete_=True,... | 1,648 | 30.113208 | 73 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py | _base_ = './mask_rcnn_r101_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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'... | 369 | 25.428571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py | _base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnext101_32x8d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dic... | 2,069 | 31.34375 | 77 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,526 | 32.195652 | 77 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py | _base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnext101_32x8d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dic... | 1,775 | 29.101695 | 77 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
total_epochs = 24
| 129 | 25 | 60 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py | _base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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(typ... | 375 | 25.857143 | 53 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 150 | 29.2 | 57 | py |
GFocalV2 | GFocalV2-master/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'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dic... | 1,413 | 34.35 | 77 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,476 | 34.166667 | 77 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnet50_caffe_bgr',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),
rpn_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
bbox_roi_extractor=... | 1,979 | 33.137931 | 78 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py | _base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnext101_32x8d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dic... | 1,826 | 28.467742 | 77 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py | _base_ = './mask_rcnn_r50_fpn_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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'... | 369 | 25.428571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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(typ... | 375 | 25.857143 | 53 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
total_epochs = 36
| 129 | 25 | 60 | py |
GFocalV2 | GFocalV2-master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,332 | 35.027027 | 77 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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(t... | 377 | 26 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = ... | 1,309 | 32.589744 | 72 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_... | 1,348 | 33.589744 | 77 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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='... | 372 | 25.642857 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 122 | 40 | 76 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 128 | 42 | 76 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 158 | 30.8 | 57 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
type='CascadeRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 395 | 25.4 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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(ty... | 376 | 25.928571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
type='CascadeRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 396 | 25.466667 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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='B... | 371 | 25.571429 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 153 | 29.8 | 57 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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(t... | 377 | 26 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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(ty... | 376 | 25.928571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 123 | 40.333333 | 76 | py |
GFocalV2 | GFocalV2-master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 127 | 41.666667 | 76 | py |
GFocalV2 | GFocalV2-master/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='NASFCOS',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indi... | 2,866 | 27.959596 | 73 | py |
GFocalV2 | GFocalV2-master/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='NASFCOS',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indi... | 2,888 | 27.89 | 73 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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', requi... | 362 | 24.928571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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', requi... | 362 | 24.928571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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', requi... | 362 | 24.928571 | 53 | py |
GFocalV2 | GFocalV2-master/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'
]
# dataset settings
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type=... | 1,352 | 33.692308 | 72 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
tra... | 1,328 | 33.973684 | 75 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_r101_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 113 | 37 | 76 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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', requi... | 362 | 24.928571 | 53 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py | _base_ = './rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 144 | 28 | 57 | py |
GFocalV2 | GFocalV2-master/configs/rpn/rpn_r101_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 113 | 37 | 76 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.