repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1 value |
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DDOD | DDOD-main/configs/retinanet/retinanet_r50_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 146 | 28.4 | 53 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 419 | 27 | 76 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './retinanet_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),
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']),
]
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))
| 1,552 | 32.042553 | 72 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 160 | 31.2 | 55 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py | _base_ = './retinanet_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),
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))
| 1,408 | 32.547619 | 72 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_r101_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 160 | 31.2 | 55 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 419 | 27 | 76 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_r101_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_r50_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'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 260 | 31.625 | 72 | py |
DDOD | DDOD-main/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 419 | 27 | 76 | py |
DDOD | DDOD-main/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 377 | 26 | 76 | py |
DDOD | DDOD-main/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 209 | 29 | 61 | py |
DDOD | DDOD-main/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='FreeAnchorRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 775 | 32.73913 | 74 | py |
DDOD | DDOD-main/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residual=False,
norm_cfg=norm_cfg,
init_cfg=None),
neck=dict(norm_cfg=norm_cfg),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg)))
# optimizer
optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
optimizer_config = dict(_delete_=True, grad_clip=None)
# learning policy
lr_config = dict(warmup_ratio=0.1, step=[65, 71])
runner = dict(type='EpochBasedRunner', max_epochs=73)
| 816 | 31.68 | 72 | py |
DDOD | DDOD-main/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residual=False,
norm_cfg=norm_cfg,
init_cfg=None),
neck=dict(norm_cfg=norm_cfg),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg),
mask_head=dict(norm_cfg=norm_cfg)))
# optimizer
optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
optimizer_config = dict(_delete_=True, grad_clip=None)
# learning policy
lr_config = dict(warmup_ratio=0.1, step=[65, 71])
runner = dict(type='EpochBasedRunner', max_epochs=73)
| 856 | 31.961538 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 224 | 27.125 | 67 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 421 | 27.133333 | 76 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
# 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, 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))
| 1,526 | 29.54 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# 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))
| 1,304 | 33.342105 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
classes = ('person', 'bicycle', 'car')
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa
| 476 | 46.7 | 209 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))
| 118 | 38.666667 | 73 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './faster_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')))
# 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, 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']),
]
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))
| 1,554 | 32.085106 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py | _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 162 | 31.6 | 57 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 468 | 26.588235 | 77 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0))))
| 207 | 28.714286 | 70 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
classes = ('person', )
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa
| 460 | 45.1 | 209 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
| 177 | 28.666667 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
| 91 | 22 | 77 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py | _base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=10000)
evaluation = dict(interval=10000, metric='bbox')
| 380 | 22.8125 | 69 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_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'
]
| 177 | 28.666667 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_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(
test_cfg=dict(
rcnn=dict(
score_thr=0.05,
nms=dict(type='soft_nms', iou_threshold=0.5),
max_per_img=100)))
| 347 | 25.769231 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 421 | 27.133333 | 76 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 162 | 31.6 | 57 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.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),
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']),
])
]
# Use RepeatDataset to speed up training
data = dict(
train=dict(dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,923 | 29.539683 | 77 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './faster_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')))
# 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))
| 1,410 | 32.595238 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 206 | 24.875 | 61 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='GIoULoss', loss_weight=10.0))))
| 201 | 27.857143 | 64 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = 'faster_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)
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))
| 1,505 | 30.375 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 421 | 27.133333 | 76 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
| 200 | 27.714286 | 63 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# 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))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,388 | 33.725 | 72 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 421 | 27.133333 | 76 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 468 | 26.588235 | 77 | py |
DDOD | DDOD-main/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=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']),
]
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))
| 1,448 | 32.697674 | 72 | py |
DDOD | DDOD-main/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py | _base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(
type='FPG',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
stack_times=9,
paths=['bu'] * 9,
same_down_trans=None,
same_up_trans=dict(
type='conv',
kernel_size=3,
stride=2,
padding=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_lateral_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_down_trans=dict(
type='interpolation_conv',
mode='nearest',
kernel_size=3,
norm_cfg=norm_cfg,
order=('act', 'conv', 'norm'),
inplace=False),
across_up_trans=None,
across_skip_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
output_trans=dict(
type='last_conv',
kernel_size=3,
order=('act', 'conv', 'norm'),
inplace=False),
norm_cfg=norm_cfg,
skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
| 1,450 | 28.612245 | 64 | py |
DDOD | DDOD-main/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py | _base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(
_delete_=True,
type='FPG',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
add_extra_convs=True,
start_level=1,
stack_times=9,
paths=['bu'] * 9,
same_down_trans=None,
same_up_trans=dict(
type='conv',
kernel_size=3,
stride=2,
padding=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_lateral_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_down_trans=dict(
type='interpolation_conv',
mode='nearest',
kernel_size=3,
norm_cfg=norm_cfg,
order=('act', 'conv', 'norm'),
inplace=False),
across_up_trans=None,
across_skip_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
output_trans=dict(
type='last_conv',
kernel_size=3,
order=('act', 'conv', 'norm'),
inplace=False),
norm_cfg=norm_cfg,
skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
evaluation = dict(interval=2)
| 1,571 | 28.111111 | 64 | py |
DDOD | DDOD-main/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
neck=dict(norm_cfg=norm_cfg),
roi_head=dict(bbox_head=dict(norm_cfg=norm_cfg)))
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, with_mask=True),
dict(
type='Resize',
img_scale=(640, 640),
ratio_range=(0.8, 1.2),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(640, 640)),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=(640, 640)),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=64),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# learning policy
optimizer = dict(
type='SGD',
lr=0.08,
momentum=0.9,
weight_decay=0.0001,
paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.1,
step=[30, 40])
# runtime settings
runner = dict(max_epochs=50)
evaluation = dict(interval=2)
| 2,140 | 30.028986 | 77 | py |
DDOD | DDOD-main/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
norm_cfg=norm_cfg,
num_outs=5),
roi_head=dict(
bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg)))
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, with_mask=True),
dict(
type='Resize',
img_scale=(640, 640),
ratio_range=(0.8, 1.2),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(640, 640)),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=(640, 640)),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=64),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# learning policy
optimizer = dict(
type='SGD',
lr=0.08,
momentum=0.9,
weight_decay=0.0001,
paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.1,
step=[30, 40])
# runtime settings
runner = dict(max_epochs=50)
evaluation = dict(interval=2)
| 2,312 | 29.84 | 78 | py |
DDOD | DDOD-main/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py | _base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py'
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128),
mask_roi_extractor=dict(out_channels=128),
mask_head=dict(in_channels=128)))
| 357 | 31.545455 | 52 | py |
DDOD | DDOD-main/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py | _base_ = 'retinanet_r50_fpg_crop640_50e_coco.py'
model = dict(
neck=dict(out_channels=128, inter_channels=128),
bbox_head=dict(in_channels=128))
| 154 | 24.833333 | 52 | py |
DDOD | DDOD-main/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py | _base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128)))
| 314 | 30.5 | 52 | py |
DDOD | DDOD-main/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py | _base_ = 'faster_rcnn_r50_fpn_crop640_50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(
type='FPG',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
stack_times=9,
paths=['bu'] * 9,
same_down_trans=None,
same_up_trans=dict(
type='conv',
kernel_size=3,
stride=2,
padding=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_lateral_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_down_trans=dict(
type='interpolation_conv',
mode='nearest',
kernel_size=3,
norm_cfg=norm_cfg,
order=('act', 'conv', 'norm'),
inplace=False),
across_up_trans=None,
across_skip_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
output_trans=dict(
type='last_conv',
kernel_size=3,
order=('act', 'conv', 'norm'),
inplace=False),
norm_cfg=norm_cfg,
skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
| 1,452 | 28.653061 | 64 | py |
DDOD | DDOD-main/configs/sabl/sabl_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'
]
# model settings
model = dict(
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
]))
| 3,155 | 35.275862 | 79 | py |
DDOD | DDOD-main/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
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']),
]
data = dict(train=dict(pipeline=train_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 2,474 | 32.445946 | 77 | py |
DDOD | DDOD-main/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,619 | 30.764706 | 73 | py |
DDOD | DDOD-main/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,849 | 31.45614 | 73 | py |
DDOD | DDOD-main/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
]))
| 3,296 | 35.230769 | 79 | py |
DDOD | DDOD-main/configs/sabl/sabl_faster_rcnn_r50_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(
roi_head=dict(
bbox_head=dict(
_delete_=True,
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0))))
| 1,228 | 34.114286 | 77 | py |
DDOD | DDOD-main/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,708 | 31.245283 | 73 | py |
DDOD | DDOD-main/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
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='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']),
]
data = dict(train=dict(pipeline=train_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 2,474 | 32.445946 | 77 | py |
DDOD | DDOD-main/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
roi_head=dict(
bbox_head=dict(
_delete_=True,
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0))))
| 1,369 | 34.128205 | 77 | py |
DDOD | DDOD-main/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,760 | 31.018182 | 73 | py |
DDOD | DDOD-main/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5))
| 200 | 21.333333 | 56 | py |
DDOD | DDOD-main/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# actual epoch = 3 * 3 = 9
lr_config = dict(policy='step', step=[3])
# runtime settings
runner = dict(
type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12
| 506 | 32.8 | 79 | py |
DDOD | DDOD-main/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/VOCdevkit/'
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=(1000, 600), 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=(1000, 600),
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=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file='data/voc0712_trainval.json',
img_prefix='data/VOCdevkit',
pipeline=train_pipeline,
classes=CLASSES)),
val=dict(
type=dataset_type,
ann_file='data/voc07_test.json',
img_prefix='data/VOCdevkit',
pipeline=test_pipeline,
classes=CLASSES),
test=dict(
type=dataset_type,
ann_file='data/voc07_test.json',
img_prefix='data/VOCdevkit',
pipeline=test_pipeline,
classes=CLASSES))
evaluation = dict(interval=1, metric='bbox')
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# actual epoch = 3 * 3 = 9
lr_config = dict(policy='step', step=[3])
# runtime settings
runner = dict(
type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12
| 2,524 | 32.223684 | 79 | py |
DDOD | DDOD-main/configs/pascal_voc/ssd512_voc0712.py | _base_ = 'ssd300_voc0712.py'
input_size = 512
model = dict(
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
anchor_generator=dict(
input_size=input_size,
strides=[8, 16, 32, 64, 128, 256, 512],
basesize_ratio_range=(0.15, 0.9),
ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]))))
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
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='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(512, 512),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
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))
| 1,789 | 32.773585 | 79 | py |
DDOD | DDOD-main/configs/pascal_voc/ssd300_voc0712.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
0.9))))
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
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='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(300, 300),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=3,
train=dict(
type='RepeatDataset', times=10, dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict()
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 20])
checkpoint_config = dict(interval=1)
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 2,239 | 31 | 79 | py |
DDOD | DDOD-main/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# actual epoch = 3 * 3 = 9
lr_config = dict(policy='step', step=[3])
# runtime settings
runner = dict(
type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12
| 489 | 31.666667 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r50_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'
]
| 174 | 28.166667 | 72 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py | _base_ = './mask_rcnn_r101_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')))
| 420 | 27.066667 | 76 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'))
| 434 | 23.166667 | 53 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py | _base_ = './mask_rcnn_r101_fpn_1x_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')))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 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, 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(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline),
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))
| 2,132 | 31.318182 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_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,
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))
| 1,606 | 31.14 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py | _base_ = './mask_rcnn_r101_fpn_1x_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')))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
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, 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))
| 1,838 | 29.147541 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 165 | 32.2 | 60 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py | _base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 426 | 27.466667 | 76 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './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')))
| 222 | 26.875 | 67 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# 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))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,413 | 34.35 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_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, 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))
| 1,556 | 32.847826 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.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://resnet50_caffe_bgr')),
rpn_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
bbox_roi_extractor=dict(
roi_layer=dict(
type='RoIAlign',
output_size=7,
sampling_ratio=2,
aligned=False)),
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
roi_layer=dict(
type='RoIAlign',
output_size=14,
sampling_ratio=2,
aligned=False))))
# 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, 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))
| 2,047 | 32.57377 | 78 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_32x8d_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=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')))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
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,
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']),
])
]
# Use RepeatDataset to speed up training
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
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))
| 2,537 | 28.511628 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py | _base_ = './mask_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_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'
]
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,
poly2mask=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', 'gt_labels', 'gt_masks']),
]
data = dict(train=dict(pipeline=train_pipeline))
| 805 | 32.583333 | 77 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 420 | 27.066667 | 76 | py |
DDOD | DDOD-main/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(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'))
| 434 | 23.166667 | 53 | py |
DDOD | DDOD-main/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(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 185 | 25.571429 | 76 | py |
DDOD | DDOD-main/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 426 | 27.466667 | 76 | py |
DDOD | DDOD-main/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 |
DDOD | DDOD-main/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(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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))
| 1,597 | 28.592593 | 77 | py |
DDOD | DDOD-main/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 |
DDOD | DDOD-main/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 |
DDOD | DDOD-main/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 |
DDOD | DDOD-main/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 |
DDOD | DDOD-main/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 |
DDOD | DDOD-main/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 |
DDOD | DDOD-main/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 |
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