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|---|---|---|---|---|---|---|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py
|
_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 420
| 27.066667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_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(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 485
| 24.578947
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_r101_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(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 263
| 23
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py
|
_base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 426
| 27.466667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_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'
]
| 174
| 28.166667
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_r101_caffe_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(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_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 = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
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', '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))
| 1,660
| 28.660714
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/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])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 165
| 32.2
| 60
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
|
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
| 107
| 20.6
| 49
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
|
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_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 = [
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))
| 1,412
| 33.463415
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py
|
_base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 272
| 33.125
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
sampler=dict(
type='ScoreHLRSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
k=0.5,
bias=0.),
isr=dict(k=2, bias=0),
carl=dict(k=1, bias=0.2))),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0)))
| 929
| 29
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py
|
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 265
| 32.25
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
sampler=dict(
type='ScoreHLRSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
k=0.5,
bias=0.),
isr=dict(k=2, bias=0),
carl=dict(k=1, bias=0.2))),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0)))
| 926
| 28.903226
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_ssd512_coco.py
|
_base_ = '../ssd/ssd512_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 247
| 26.555556
| 71
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_ssd300_coco.py
|
_base_ = '../ssd/ssd300_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 247
| 26.555556
| 71
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
sampler=dict(
type='ScoreHLRSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
k=0.5,
bias=0.),
isr=dict(k=2, bias=0),
carl=dict(k=1, bias=0.2))),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0)))
| 933
| 29.129032
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
sampler=dict(
type='ScoreHLRSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
k=0.5,
bias=0.),
isr=dict(k=2, bias=0),
carl=dict(k=1, bias=0.2))),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0)))
| 922
| 28.774194
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 428
| 27.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 435
| 28.066667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
|
_base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
| 183
| 29.666667
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_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 = [
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))
| 1,389
| 31.325581
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
|
_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
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='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))
| 1,426
| 32.97619
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 435
| 28.066667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 423
| 27.266667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 200
| 27.714286
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
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=dict(type='BN', requires_grad=False),
style='pytorch',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
to_rgb=False)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
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']),
]
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))
| 1,878
| 29.803279
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 213
| 29.571429
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 182
| 29.5
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 206
| 28.571429
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 230
| 27.875
| 67
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 446
| 26.9375
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 427
| 27.533333
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 447
| 27
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 149
| 29
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 422
| 27.2
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py
|
_base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
| 110
| 21.2
| 51
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 238
| 28.875
| 67
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_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'
]
| 178
| 28.833333
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
|
_base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 225
| 27.25
| 67
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 428
| 27.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 427
| 27.533333
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
|
_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201
| 27.857143
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 205
| 28.428571
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
|
_base_ = ['./cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py']
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_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)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
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']),
]
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))
| 1,631
| 31.64
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/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',
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=False, eps=0),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
neck=dict(
type='NASFCOS_FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs=True,
num_outs=5,
norm_cfg=dict(type='BN'),
conv_cfg=dict(type='DCNv2', deform_groups=2)),
bbox_head=dict(
type='NASFCOSHead',
num_classes=80,
in_channels=256,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
norm_cfg=dict(type='GN', num_groups=32),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
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))
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='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(
samples_per_gpu=4,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(
lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
| 2,990
| 28.91
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/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',
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=False, eps=0),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
neck=dict(
type='NASFCOS_FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs=True,
num_outs=5,
norm_cfg=dict(type='BN'),
conv_cfg=dict(type='DCNv2', deform_groups=2)),
bbox_head=dict(
type='FCOSHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
norm_cfg=dict(type='GN', num_groups=32),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
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))
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='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(
samples_per_gpu=4,
workers_per_gpu=2,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
optimizer = dict(
lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
| 3,012
| 28.831683
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py
|
_base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 413
| 26.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py
|
_base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 413
| 26.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py
|
_base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 413
| 26.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_r50_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
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_label=False),
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']),
]
data = dict(train=dict(pipeline=train_pipeline))
evaluation = dict(interval=1, metric='proposal_fast')
| 776
| 39.894737
| 78
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/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='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_label=False),
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']),
]
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))
evaluation = dict(interval=1, metric='proposal_fast')
| 1,352
| 33.692308
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py
|
_base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_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 = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_label=False),
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']),
]
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))
| 1,407
| 32.52381
| 72
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_r101_fpn_1x_coco.py
|
_base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191
| 26.428571
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_r50_fpn_2x_coco.py
|
_base_ = './rpn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 141
| 22.666667
| 53
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py
|
_base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 413
| 26.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py
|
_base_ = './rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 216
| 26.125
| 67
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/rpn/rpn_r101_fpn_2x_coco.py
|
_base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191
| 26.428571
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DeformableDETR',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='ChannelMapper',
in_channels=[512, 1024, 2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=4),
bbox_head=dict(
type='DeformableDETRHead',
num_query=300,
num_classes=80,
in_channels=2048,
sync_cls_avg_factor=True,
as_two_stage=False,
transformer=dict(
type='DeformableDetrTransformer',
encoder=dict(
type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention', embed_dims=256),
feedforward_channels=1024,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DeformableDetrTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.1),
dict(
type='MultiScaleDeformableAttention',
embed_dims=256)
],
feedforward_channels=1024,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')))),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=128,
normalize=True,
offset=-0.5),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
test_cfg=dict(max_per_img=100))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=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',
# The radio of all image in train dataset < 7
# follow the original impl
img_scale=[(400, 4200), (500, 4200), (600, 4200)],
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=1),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
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=1),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(filter_empty_gt=False, pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
type='AdamW',
lr=2e-4,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys={
'backbone': dict(lr_mult=0.1),
'sampling_offsets': dict(lr_mult=0.1),
'reference_points': dict(lr_mult=0.1)
}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
# learning policy
lr_config = dict(policy='step', step=[40])
runner = dict(type='EpochBasedRunner', max_epochs=50)
| 6,478
| 36.450867
| 79
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py
|
_base_ = 'deformable_detr_r50_16x2_50e_coco.py'
model = dict(bbox_head=dict(with_box_refine=True))
| 99
| 32.333333
| 50
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py
|
_base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py'
model = dict(bbox_head=dict(as_two_stage=True))
| 103
| 33.666667
| 54
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/res2net/htc_r2_101_fpn_20e_coco.py
|
_base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 379
| 26.142857
| 62
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py
|
_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 294
| 25.818182
| 62
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 287
| 25.181818
| 62
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
|
_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 291
| 25.545455
| 62
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py
|
_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 299
| 26.272727
| 64
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
|
_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 441
| 28.466667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
|
_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 221
| 30.714286
| 65
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
|
_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 443
| 28.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v0.5_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)),
test_cfg=dict(
rcnn=dict(
score_thr=0.0001,
# LVIS allows up to 300
max_per_img=300)))
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, 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']),
]
data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
| 1,162
| 35.34375
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
|
_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 443
| 28.6
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
|
_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 441
| 28.466667
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py
|
_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 219
| 30.428571
| 63
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=dict(
score_thr=0.0001,
# LVIS allows up to 300
max_per_img=300)))
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, 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']),
]
data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
| 1,160
| 35.28125
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py
|
_base_ = './yolof_r50_c5_8x8_1x_coco.py'
# We implemented the iter-based config according to the source code.
# COCO dataset has 117266 images after filtering. We use 8 gpu and
# 8 batch size training, so 22500 is equivalent to
# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch,
# 20000 is equivalent to 10.9 epoch. Due to lr(0.12) is large,
# the iter-based and epoch-based setting have about 0.2 difference on
# the mAP evaluation value.
lr_config = dict(step=[15000, 20000])
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=22500)
checkpoint_config = dict(interval=2500)
evaluation = dict(interval=4500)
log_config = dict(interval=20)
| 671
| 43.8
| 69
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/yolof/yolof_r50_c5_8x8_1x_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet50_caffe')),
neck=dict(
type='DilatedEncoder',
in_channels=2048,
out_channels=512,
block_mid_channels=128,
num_residual_blocks=4),
bbox_head=dict(
type='YOLOFHead',
num_classes=80,
in_channels=512,
reg_decoded_bbox=True,
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[1, 2, 4, 8, 16],
strides=[32]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1., 1., 1., 1.],
add_ctr_clamp=True,
ctr_clamp=32),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='UniformAssigner', pos_ignore_thr=0.15, neg_ignore_thr=0.7),
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))
# optimizer
optimizer = dict(
type='SGD',
lr=0.12,
momentum=0.9,
weight_decay=0.0001,
paramwise_cfg=dict(
norm_decay_mult=0., custom_keys={'backbone': dict(lr_mult=1. / 3)}))
lr_config = dict(warmup_iters=1500, warmup_ratio=0.00066667)
# 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 = [
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='RandomShift', shift_ratio=0.5, max_shift_px=32),
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(
samples_per_gpu=8,
workers_per_gpu=8,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 3,279
| 29.943396
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py
|
_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 390
| 31.583333
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 256
| 27.555556
| 56
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 369
| 29.833333
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 162
| 31.6
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 169
| 33
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 375
| 30.333333
| 60
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
|
_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 180
| 35.2
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
|
_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 387
| 31.333333
| 70
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 370
| 29.916667
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 257
| 27.666667
| 57
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 369
| 29.833333
| 60
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 163
| 31.8
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 258
| 27.777778
| 57
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
|
_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 389
| 31.5
| 73
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py
|
_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 183
| 35.8
| 75
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 376
| 30.416667
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 368
| 29.75
| 60
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
|
_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 386
| 31.25
| 70
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 257
| 27.666667
| 56
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
|
_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 430
| 27.733333
| 76
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py
|
_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 208
| 28.857143
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 217
| 26.25
| 61
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py
|
_base_ = '../cascade_rcnn/cascade_mask_rcnn_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='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
dx=15,
dy=15,
theta=(-1, 1),
color_prob=0.5,
hflag=False,
aug_ratio=0.5),
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']),
]
data = dict(train=dict(pipeline=train_pipeline))
# learning policy
lr_config = dict(step=[32, 44])
runner = dict(type='EpochBasedRunner', max_epochs=48)
| 1,023
| 34.310345
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py
|
_base_ = '../mask_rcnn/mask_rcnn_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='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
dx=15,
dy=15,
theta=(-1, 1),
color_prob=0.5,
hflag=False,
aug_ratio=0.5),
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']),
]
data = dict(train=dict(pipeline=train_pipeline))
# learning policy
lr_config = dict(step=[32, 44])
runner = dict(type='EpochBasedRunner', max_epochs=48)
| 1,012
| 33.931034
| 77
|
py
|
PseCo
|
PseCo-master/thirdparty/mmdetection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
|
_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 438
| 28.266667
| 76
|
py
|
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