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| max_line_length
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stringclasses 1
value |
|---|---|---|---|---|---|---|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 158 | 30.8 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py
|
_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 457 | 37.166667 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
|
_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256,
stride=2,
num_outs=5))
img_norm_cfg = dict(
mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 2,333 | 31.873239 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 463 | 37.666667 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py
|
_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 1,291 | 30.512195 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 459 | 40.818182 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 484 | 39.416667 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py
|
_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
| 158 | 30.8 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 153 | 29.8 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
|
_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 462 | 37.583333 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 443 | 39.363636 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 455 | 37 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
| 1,185 | 30.210526 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
img_norm_cfg = dict(
mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 1,337 | 32.45 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py
|
_base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 158 | 30.8 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py
|
_base_ = './htc_hrnetv2p_w32_20e_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 431 | 38.272727 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py
|
_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 487 | 36.538462 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py
|
_base_ = './htc_hrnetv2p_w40_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
| 146 | 28.4 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py
|
_base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 153 | 29.8 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py
|
_base_ = '../htc/htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
neck=dict(
_delete_=True,
type='HRFPN',
in_channels=[32, 64, 128, 256],
out_channels=256))
| 1,170 | 29.815789 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py
|
_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
| 436 | 38.727273 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py
|
_base_ = './htc_hrnetv2p_w32_20e_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
| 456 | 37.083333 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
]))
| 403 | 27.857143 | 56 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
],
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 575 | 32.882353 | 72 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
],
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 575 | 32.882353 | 72 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
stages=(False, False, True, True),
position='after_conv2')
]))
| 403 | 27.857143 | 56 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/yolox/yolox_m_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.67, widen_factor=0.75),
neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2),
bbox_head=dict(in_channels=192, feat_channels=192),
)
| 266 | 28.666667 | 79 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/yolox/yolox_s_8x8_300e_coco.py
|
_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
img_scale = (640, 640)
# model settings
model = dict(
type='YOLOX',
input_size=img_scale,
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
neck=dict(
type='YOLOXPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
num_csp_blocks=1),
bbox_head=dict(
type='YOLOXHead', num_classes=80, in_channels=128, feat_channels=128),
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
# In order to align the source code, the threshold of the val phase is
# 0.01, and the threshold of the test phase is 0.001.
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
# dataset settings
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
train_pipeline = [
dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='MixUp',
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
# According to the official implementation, multi-scale
# training is not considered here but in the
# 'mmdet/models/detectors/yolox.py'.
dict(type='Resize', img_scale=img_scale, keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
# If the image is three-channel, the pad value needs
# to be set separately for each channel.
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
train_dataset = dict(
type='MultiImageMixDataset',
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False,
),
pipeline=train_pipeline)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=img_scale,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
persistent_workers=True,
train=train_dataset,
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
# optimizer
# default 8 gpu
optimizer = dict(
type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=5e-4,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
optimizer_config = dict(grad_clip=None)
max_epochs = 300
num_last_epochs = 15
resume_from = None
interval = 10
# learning policy
lr_config = dict(
_delete_=True,
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5, # 5 epoch
num_last_epochs=num_last_epochs,
min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
custom_hooks = [
dict(
type='YOLOXModeSwitchHook',
num_last_epochs=num_last_epochs,
priority=48),
dict(
type='SyncNormHook',
num_last_epochs=num_last_epochs,
interval=interval,
priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=resume_from,
momentum=0.0001,
priority=49)
]
checkpoint_config = dict(interval=interval)
evaluation = dict(
save_best='auto',
# The evaluation interval is 'interval' when running epoch is
# less than ‘max_epochs - num_last_epochs’.
# The evaluation interval is 1 when running epoch is greater than
# or equal to ‘max_epochs - num_last_epochs’.
interval=interval,
dynamic_intervals=[(max_epochs - num_last_epochs, 1)],
metric='bbox')
log_config = dict(interval=50)
| 4,793 | 28.776398 | 79 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/yolox/yolox_l_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.0, widen_factor=1.0),
neck=dict(
in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3),
bbox_head=dict(in_channels=256, feat_channels=256))
| 272 | 29.333333 | 74 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/yolox/yolox_x_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.33, widen_factor=1.25),
neck=dict(
in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4),
bbox_head=dict(in_channels=320, feat_channels=320))
| 274 | 29.555556 | 74 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/yolox/yolox_nano_8x8_300e_coco.py
|
_base_ = './yolox_tiny_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True),
neck=dict(
in_channels=[64, 128, 256],
out_channels=64,
num_csp_blocks=1,
use_depthwise=True),
bbox_head=dict(in_channels=64, feat_channels=64, use_depthwise=True))
| 356 | 28.75 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/yolox/yolox_tiny_8x8_300e_coco.py
|
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
random_size_range=(10, 20),
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
img_scale = (640, 640)
train_pipeline = [
dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.5, 1.5),
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=img_scale, keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(416, 416),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
train_dataset = dict(pipeline=train_pipeline)
data = dict(
train=train_dataset,
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,617 | 28.962963 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/swin/retinanet_swin-t-p4-w7_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'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(1, 2, 3),
# Please only add indices that would be used
# in FPN, otherwise some parameter will not be used
with_cp=False,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(in_channels=[192, 384, 768], start_level=0, num_outs=5))
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,043 | 33.8 | 123 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type='MaskRCNN',
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(in_channels=[96, 192, 384, 768]))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# augmentation strategy originates from DETR / Sparse RCNN
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[[
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
multiscale_mode='value',
keep_ratio=True)
],
[
dict(
type='Resize',
img_scale=[(400, 1333), (500, 1333), (600, 1333)],
multiscale_mode='value',
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
override=True,
keep_ratio=True)
]]),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
data = dict(train=dict(pipeline=train_pipeline))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
lr_config = dict(warmup_iters=1000, step=[27, 33])
runner = dict(max_epochs=36)
| 3,305 | 34.934783 | 123 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
|
_base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
depths=[2, 2, 18, 2],
init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| 318 | 44.571429 | 124 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type='MaskRCNN',
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(in_channels=[96, 192, 384, 768]))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
lr_config = dict(warmup_iters=1000, step=[8, 11])
runner = dict(max_epochs=12)
| 1,301 | 29.27907 | 123 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
|
_base_ = './mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py'
# you need to set mode='dynamic' if you are using pytorch<=1.5.0
fp16 = dict(loss_scale=dict(init_scale=512))
| 169 | 41.5 | 64 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 464 | 26.352941 | 62 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 546 | 33.1875 | 74 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_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='VFNet',
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_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output', # use P5
num_outs=5,
relu_before_extra_convs=True),
bbox_head=dict(
type='VFNetHead',
num_classes=80,
in_channels=256,
stacked_convs=3,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
center_sampling=False,
dcn_on_last_conv=False,
use_atss=True,
use_vfl=True,
loss_cls=dict(
type='VarifocalLoss',
use_sigmoid=True,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(type='ATSSAssigner', topk=9),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
# data setting
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.1,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 3,240 | 29.009259 | 79 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 602 | 30.736842 | 74 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 27.857143 | 61 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 585 | 31.555556 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 480), (1333, 960)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 1,312 | 31.825 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(dcn_on_last_conv=True))
| 248 | 34.571429 | 74 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r101_fpn_1x_coco.py
|
_base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 193 | 26.714286 | 61 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_r101_fpn_2x_coco.py
|
_base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 279 | 30.111111 | 61 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 447 | 27 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_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),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 585 | 31.555556 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py
|
_base_ = './vfnet_r50_fpn_mstrain_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),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 447 | 27 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
backbone=dict(
type='ResNet',
depth=18,
norm_eval=False,
norm_cfg=dict(type='BN'),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(
type='CTResNetNeck',
in_channel=512,
num_deconv_filters=(256, 128, 64),
num_deconv_kernels=(4, 4, 4),
use_dcn=True),
bbox_head=dict(
type='CenterNetHead',
num_classes=80,
in_channel=64,
feat_channel=64,
loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0),
loss_wh=dict(type='L1Loss', loss_weight=0.1),
loss_offset=dict(type='L1Loss', loss_weight=1.0)),
train_cfg=None,
test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100))
# We fixed the incorrect img_norm_cfg problem in the source code.
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True, color_type='color'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(
type='RandomCenterCropPad',
crop_size=(512, 512),
ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
mean=[0, 0, 0],
std=[1, 1, 1],
to_rgb=True,
test_pad_mode=None),
dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='MultiScaleFlipAug',
scale_factor=1.0,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='RandomCenterCropPad',
ratios=None,
border=None,
mean=[0, 0, 0],
std=[1, 1, 1],
to_rgb=True,
test_mode=True,
test_pad_mode=['logical_or', 31],
test_pad_add_pix=1),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
'scale_factor', 'flip', 'flip_direction',
'img_norm_cfg', 'border'),
keys=['img'])
])
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Use RepeatDataset to speed up training
data = dict(
samples_per_gpu=16,
workers_per_gpu=4,
train=dict(
_delete_=True,
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
# Based on the default settings of modern detectors, the SGD effect is better
# than the Adam in the source code, so we use SGD default settings and
# if you use adam+lr5e-4, the map is 29.1.
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
# Based on the default settings of modern detectors, we added warmup settings.
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=1.0 / 1000,
step=[18, 24]) # the real step is [18*5, 24*5]
runner = dict(max_epochs=28) # the real epoch is 28*5=140
| 4,045 | 31.894309 | 79 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/centernet/centernet_resnet18_140e_coco.py
|
_base_ = './centernet_resnet18_dcnv2_140e_coco.py'
model = dict(neck=dict(use_dcn=False))
| 91 | 22 | 50 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py
|
_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 146 | 28.4 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
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_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
num_outs=5,
add_extra_convs='on_input'),
bbox_head=dict(
type='FoveaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
base_edge_list=[16, 32, 64, 128, 256],
scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
sigma=0.4,
with_deform=False,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=1.50,
alpha=0.4,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
# training and testing settings
train_cfg=dict(),
test_cfg=dict(
nms_pre=1000,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))
data = dict(samples_per_gpu=4, workers_per_gpu=4)
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,612 | 29.433962 | 79 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py
|
_base_ = './fovea_r50_fpn_4x4_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
data = dict(train=dict(pipeline=train_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 1,042 | 33.766667 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
|
_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
data = dict(train=dict(pipeline=train_pipeline))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 901 | 33.692308 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py
|
_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
|
_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 417 | 31.153846 | 69 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
|
_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 362 | 32 | 69 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DoubleHeadRoIHead',
reg_roi_scale_factor=1.3,
bbox_head=dict(
_delete_=True,
type='DoubleConvFCBBoxHead',
num_convs=4,
num_fcs=2,
in_channels=256,
conv_out_channels=1024,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0))))
| 845 | 34.25 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
|
_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 384],
out_channels=256,
num_outs=5))
| 533 | 28.666667 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py
|
_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_8.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 720, 1920],
out_channels=256,
num_outs=5))
| 521 | 28 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
|
_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 384],
out_channels=256,
num_outs=5))
| 527 | 28.333333 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py
|
_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
| 520 | 27.944444 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py
|
_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_12gf')),
neck=dict(
type='FPN',
in_channels=[224, 448, 896, 2240],
out_channels=256,
num_outs=5))
| 520 | 27.944444 | 72 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py
|
_base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 140 | 34.25 | 53 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py
|
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 760 | 27.185185 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
|
_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
| 528 | 28.388889 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
|
_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
| 520 | 27.944444 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py
|
_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
| 521 | 28 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
|
_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
| 528 | 28.388889 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
|
_base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
img_norm_cfg = dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(weight_decay=0.00005)
| 1,888 | 29.467742 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py
|
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 384],
out_channels=256,
num_outs=5))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 759 | 27.148148 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py
|
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 761 | 27.222222 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
|
_base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
| 305 | 37.25 | 74 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
|
_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
| 534 | 28.722222 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
|
_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
| 535 | 28.777778 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
img_norm_cfg = dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 2,004 | 32.416667 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
|
_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
| 529 | 28.444444 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
|
_base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
img_norm_cfg = dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
to_rgb=False)
train_pipeline = [
# Images are converted to float32 directly after loading in PyCls
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(weight_decay=0.00005)
| 2,005 | 30.34375 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
img_norm_cfg = dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
| 1,920 | 32.12069 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
img_norm_cfg = dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
to_rgb=False)
train_pipeline = [
# Images are converted to float32 directly after loading in PyCls
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
| 2,015 | 33.169492 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
img_norm_cfg = dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 2,261 | 32.761194 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py
|
_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_6.4gf')),
neck=dict(
type='FPN',
in_channels=[168, 392, 784, 1624],
out_channels=256,
num_outs=5))
| 522 | 28.055556 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py
|
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 760 | 27.185185 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
|
_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
| 534 | 28.722222 | 73 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DynamicRoIHead',
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(nms=dict(iou_threshold=0.85)),
rcnn=dict(
dynamic_rcnn=dict(
iou_topk=75,
beta_topk=10,
update_iter_interval=100,
initial_iou=0.4,
initial_beta=1.0))),
test_cfg=dict(rpn=dict(nms=dict(iou_threshold=0.85))))
| 1,051 | 35.275862 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
data = dict(train=dict(pipeline=train_pipeline))
| 1,072 | 31.515152 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))
| 418 | 28.928571 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
| 416 | 28.785714 | 76 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
data = dict(train=dict(pipeline=train_pipeline))
| 1,070 | 31.454545 | 77 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/wider_face/ssd300_wider_face.py
|
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=1))
# optimizer
optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict()
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.001,
step=[16, 20])
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=24)
log_config = dict(interval=1)
| 517 | 26.263158 | 71 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg)))
# # use ResNeSt img_norm
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=False,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,947 | 29.920635 | 79 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
|
_base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 261 | 31.75 | 78 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
|
_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=[
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_head=dict(norm_cfg=norm_cfg)))
# # use ResNeSt img_norm
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 4,255 | 34.764706 | 79 |
py
|
DSLA-DSLA
|
DSLA-DSLA/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg),
mask_head=dict(norm_cfg=norm_cfg)))
# # use ResNeSt img_norm
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 2,068 | 30.830769 | 79 |
py
|
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