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ERD
|
ERD-main/configs/vfnet/vfnet_r101_fpn_ms-2x_coco.py
|
_base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 196 | 27.142857 | 61 |
py
|
ERD
|
ERD-main/configs/vfnet/vfnet_x101-32x4d_fpn_ms-2x_coco.py
|
_base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 442 | 26.6875 | 76 |
py
|
ERD
|
ERD-main/configs/vfnet/vfnet_res2net101-mdconv-c3-c5_fpn_ms-2x_coco.py
|
_base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 597 | 30.473684 | 74 |
py
|
ERD
|
ERD-main/configs/vfnet/vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py
|
_base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(dcn_on_last_conv=True))
| 243 | 33.857143 | 74 |
py
|
ERD
|
ERD-main/configs/centernet/centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py
|
_base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 220 | 26.625 | 63 |
py
|
ERD
|
ERD-main/configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./centernet_tta.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNet',
depth=18,
norm_eval=False,
norm_cfg=dict(type='BN'),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(
type='CTResNetNeck',
in_channels=512,
num_deconv_filters=(256, 128, 64),
num_deconv_kernels=(4, 4, 4),
use_dcn=True),
bbox_head=dict(
type='CenterNetHead',
num_classes=80,
in_channels=64,
feat_channels=64,
loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0),
loss_wh=dict(type='L1Loss', loss_weight=0.1),
loss_offset=dict(type='L1Loss', loss_weight=1.0)),
train_cfg=None,
test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(
type='RandomCenterCropPad',
# The cropped images are padded into squares during training,
# but may be less than crop_size.
crop_size=(512, 512),
ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
mean=[0, 0, 0],
std=[1, 1, 1],
to_rgb=True,
test_pad_mode=None),
# Make sure the output is always crop_size.
dict(type='Resize', scale=(512, 512), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(
type='LoadImageFromFile',
backend_args={{_base_.backend_args}},
to_float32=True),
# don't need Resize
dict(
type='RandomCenterCropPad',
ratios=None,
border=None,
mean=[0, 0, 0],
std=[1, 1, 1],
to_rgb=True,
test_mode=True,
test_pad_mode=['logical_or', 31],
test_pad_add_pix=1),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'border'))
]
# Use RepeatDataset to speed up training
train_dataloader = dict(
batch_size=16,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
_delete_=True,
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args={{_base_.backend_args}},
)))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# optimizer
# Based on the default settings of modern detectors, the SGD effect is better
# than the Adam in the source code, so we use SGD default settings and
# if you use adam+lr5e-4, the map is 29.1.
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
max_epochs = 28
# learning policy
# Based on the default settings of modern detectors, we added warmup settings.
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[18, 24], # the real step is [18*5, 24*5]
gamma=0.1)
]
train_cfg = dict(max_epochs=max_epochs) # the real epoch is 28*5=140
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (16 samples per GPU)
auto_scale_lr = dict(base_batch_size=128)
| 4,299 | 30.386861 | 79 |
py
|
ERD
|
ERD-main/configs/centernet/centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py
|
_base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
batch_augments=batch_augments),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5,
init_cfg=dict(type='Caffe2Xavier', layer='Conv2d'),
relu_before_extra_convs=True),
bbox_head=dict(
type='CenterNetUpdateHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
loss_cls=dict(
type='GaussianFocalLoss',
pos_weight=0.25,
neg_weight=0.75,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
),
train_cfg=None,
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))
train_dataloader = dict(batch_size=8, num_workers=4)
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(
type='AmpOptimWrapper',
optimizer=dict(
type='SGD', lr=0.01 * 4, momentum=0.9, weight_decay=0.00004),
paramwise_cfg=dict(norm_decay_mult=0.))
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.00025,
by_epoch=False,
begin=0,
end=4000),
dict(
type='MultiStepLR',
begin=0,
end=25,
by_epoch=True,
milestones=[22, 24],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
| 2,397 | 27.547619 | 79 |
py
|
ERD
|
ERD-main/configs/centernet/centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py
|
_base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 244 | 29.625 | 79 |
py
|
ERD
|
ERD-main/configs/centernet/centernet_tta.py
|
# This is different from the TTA of official CenterNet.
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
tta_pipeline = [
dict(type='LoadImageFromFile', to_float32=True, backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
# ``RandomFlip`` must be placed before ``RandomCenterCropPad``,
# otherwise bounding box coordinates after flipping cannot be
# recovered correctly.
dict(type='RandomFlip', prob=1.),
dict(type='RandomFlip', prob=0.)
],
[
dict(
type='RandomCenterCropPad',
ratios=None,
border=None,
mean=[0, 0, 0],
std=[1, 1, 1],
to_rgb=True,
test_mode=True,
test_pad_mode=['logical_or', 31],
test_pad_add_pix=1),
],
[dict(type='LoadAnnotations', with_bbox=True)],
[
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'flip', 'flip_direction', 'border'))
]
])
]
| 1,361 | 33.05 | 79 |
py
|
ERD
|
ERD-main/configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 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://detectron2/resnet50_caffe')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5,
# There is a chance to get 40.3 after switching init_cfg,
# otherwise it is about 39.9~40.1
init_cfg=dict(type='Caffe2Xavier', layer='Conv2d'),
relu_before_extra_convs=True),
bbox_head=dict(
type='CenterNetUpdateHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
hm_min_radius=4,
hm_min_overlap=0.8,
more_pos_thresh=0.2,
more_pos_topk=9,
soft_weight_on_reg=False,
loss_cls=dict(
type='GaussianFocalLoss',
pos_weight=0.25,
neg_weight=0.75,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
),
train_cfg=None,
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))
# single-scale training is about 39.3
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.00025,
by_epoch=False,
begin=0,
end=4000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
optim_wrapper = dict(
optimizer=dict(lr=0.01),
# Experiments show that there is no need to turn on clip_grad.
paramwise_cfg=dict(norm_decay_mult=0.))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)
| 3,029 | 27.584906 | 73 |
py
|
ERD
|
ERD-main/configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py
|
_base_ = './centernet_r18-dcnv2_8xb16-crop512-140e_coco.py'
model = dict(neck=dict(use_dcn=False))
| 100 | 24.25 | 59 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r101_fpn_4xb4-1x_coco.py
|
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 198 | 27.428571 | 61 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r50_fpn_4xb4-2x_coco.py
|
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
train_cfg = dict(max_epochs=max_epochs)
| 379 | 22.75 | 79 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r101_fpn_4xb4-2x_coco.py
|
_base_ = './fovea_r50_fpn_4xb4-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 198 | 27.428571 | 61 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r101_fpn_gn-head-align_4xb4-2x_coco.py
|
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
train_cfg = dict(max_epochs=max_epochs)
| 650 | 26.125 | 79 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r50_fpn_4xb4-1x_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
num_outs=5,
add_extra_convs='on_input'),
bbox_head=dict(
type='FoveaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
base_edge_list=[16, 32, 64, 128, 256],
scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
sigma=0.4,
with_deform=False,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=1.50,
alpha=0.4,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
# training and testing settings
train_cfg=dict(),
test_cfg=dict(
nms_pre=1000,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))
train_dataloader = dict(batch_size=4, num_workers=4)
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| 1,836 | 29.616667 | 79 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py
|
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
train_cfg = dict(max_epochs=max_epochs)
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
| 572 | 26.285714 | 79 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r50_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py
|
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
train_cfg = dict(max_epochs=max_epochs)
| 901 | 28.096774 | 79 |
py
|
ERD
|
ERD-main/configs/foveabox/fovea_r101_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py
|
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
train_cfg = dict(max_epochs=max_epochs)
| 1,042 | 28.8 | 79 |
py
|
ERD
|
ERD-main/configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py
|
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DoubleHeadRoIHead',
reg_roi_scale_factor=1.3,
bbox_head=dict(
_delete_=True,
type='DoubleConvFCBBoxHead',
num_convs=4,
num_fcs=2,
in_channels=256,
conv_out_channels=1024,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0))))
| 845 | 34.25 | 77 |
py
|
ERD
|
ERD-main/configs/regnet/faster-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py
|
_base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
| 523 | 28.111111 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py
|
_base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
optim_wrapper = dict(optimizer=dict(weight_decay=0.00005))
| 856 | 28.551724 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-400MF_fpn_ms-3x_coco.py
|
_base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 384],
out_channels=256,
num_outs=5))
| 528 | 28.388889 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-800MF_fpn_ms-3x_coco.py
|
_base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
| 529 | 28.444444 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_2x_coco.py
|
_base_ = './faster-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
| 391 | 22.058824 | 79 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-400MF_fpn_ms-poly-3x_coco.py
|
_base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 384],
out_channels=256,
num_outs=5))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
clip_grad=dict(max_norm=35, norm_type=2))
| 739 | 26.407407 | 76 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-1.6GF_fpn_ms-poly-3x_coco.py
|
_base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
clip_grad=dict(max_norm=35, norm_type=2))
| 740 | 26.444444 | 76 |
py
|
ERD
|
ERD-main/configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py
|
_base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py']
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
optim_wrapper = dict(optimizer=dict(weight_decay=0.00005))
| 831 | 31 | 79 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-3.2GF-mdconv-c3-c5_fpn_1x_coco.py
|
_base_ = 'mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
| 305 | 37.25 | 74 |
py
|
ERD
|
ERD-main/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py
|
_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
| 520 | 27.944444 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
clip_grad=dict(max_norm=35, norm_type=2))
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[28, 34],
gamma=0.1)
]
| 1,826 | 28.95082 | 79 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-12GF_fpn_1x_coco.py
|
_base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_12gf')),
neck=dict(
type='FPN',
in_channels=[224, 448, 896, 2240],
out_channels=256,
num_outs=5))
| 520 | 27.944444 | 72 |
py
|
ERD
|
ERD-main/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
|
_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
| 520 | 27.944444 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/faster-rcnn_regnetx-4GF_fpn_ms-3x_coco.py
|
_base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
| 524 | 28.166667 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-4GF_fpn_ms-poly-3x_coco.py
|
_base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
clip_grad=dict(max_norm=35, norm_type=2))
| 741 | 26.481481 | 76 |
py
|
ERD
|
ERD-main/configs/regnet/faster-rcnn_regnetx-800MF_fpn_ms-3x_coco.py
|
_base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
| 523 | 28.111111 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005))
| 968 | 30.258065 | 76 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-6.4GF_fpn_1x_coco.py
|
_base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_6.4gf')),
neck=dict(
type='FPN',
in_channels=[168, 392, 784, 1624],
out_channels=256,
num_outs=5))
| 522 | 28.055556 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-4GF_fpn_1x_coco.py
|
_base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
| 521 | 28 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/faster-rcnn_regnetx-400MF_fpn_ms-3x_coco.py
|
_base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 384],
out_channels=256,
num_outs=5))
| 522 | 28.055556 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
clip_grad=dict(max_norm=35, norm_type=2))
| 1,012 | 30.65625 | 76 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
neck=dict(
type='FPN',
in_channels=[96, 192, 432, 1008],
out_channels=256,
num_outs=5))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005))
| 965 | 30.16129 | 76 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-8GF_fpn_1x_coco.py
|
_base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_8.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 720, 1920],
out_channels=256,
num_outs=5))
| 521 | 28 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/mask-rcnn_regnetx-800MF_fpn_ms-poly-3x_coco.py
|
_base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005),
clip_grad=dict(max_norm=35, norm_type=2))
| 740 | 26.444444 | 76 |
py
|
ERD
|
ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-4GF_fpn_ms-3x_coco.py
|
_base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
neck=dict(
type='FPN',
in_channels=[80, 240, 560, 1360],
out_channels=256,
num_outs=5))
| 530 | 28.5 | 73 |
py
|
ERD
|
ERD-main/configs/regnet/cascade-mask-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py
|
_base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
neck=dict(
type='FPN',
in_channels=[72, 168, 408, 912],
out_channels=256,
num_outs=5))
| 529 | 28.444444 | 73 |
py
|
ERD
|
ERD-main/configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py
|
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DynamicRoIHead',
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(nms=dict(iou_threshold=0.85)),
rcnn=dict(
dynamic_rcnn=dict(
iou_topk=75,
beta_topk=10,
update_iter_interval=100,
initial_iou=0.4,
initial_beta=1.0))),
test_cfg=dict(rpn=dict(nms=dict(iou_threshold=0.85))))
| 1,051 | 35.275862 | 77 |
py
|
ERD
|
ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-swav-pre_fpn_ms-2x_coco.py
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize', scale=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
| 811 | 30.230769 | 76 |
py
|
ERD
|
ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-swav-pre_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
| 416 | 28.785714 | 76 |
py
|
ERD
|
ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-mocov2-pre_fpn_1x_coco.py
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))
| 418 | 28.928571 | 78 |
py
|
ERD
|
ERD-main/configs/selfsup_pretrain/mask-rcnn_r50-mocov2-pre_fpn_ms-2x_coco.py
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize', scale=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
| 813 | 30.307692 | 78 |
py
|
ERD
|
ERD-main/configs/wider_face/retinanet_r50_fpn_1x_widerface.py
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/wider_face.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(bbox_head=dict(num_classes=1))
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| 342 | 30.181818 | 77 |
py
|
ERD
|
ERD-main/configs/wider_face/ssd300_8xb32-24e_widerface.py
|
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_2x.py'
]
model = dict(bbox_head=dict(num_classes=1))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
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={{_base_.model.data_preprocessor.mean}},
to_rgb={{_base_.model.data_preprocessor.bgr_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', scale=(300, 300), keep_ratio=False),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='Resize', scale=(300, 300), keep_ratio=False),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
train_dataloader = dict(
batch_size=32, num_workers=8, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(type='MultiStepLR', by_epoch=True, milestones=[16, 20], gamma=0.1)
]
# optimizer
optim_wrapper = dict(
optimizer=dict(lr=0.012, momentum=0.9, weight_decay=5e-4),
clip_grad=dict(max_norm=35, norm_type=2))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (32 samples per GPU)
auto_scale_lr = dict(base_batch_size=256)
| 2,087 | 31.123077 | 75 |
py
|
ERD
|
ERD-main/configs/resnest/mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py
|
_base_ = './mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 248 | 30.125 | 65 |
py
|
ERD
|
ERD-main/configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py
|
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
| 1,214 | 29.375 | 79 |
py
|
ERD
|
ERD-main/configs/resnest/cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py
|
_base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=[
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_head=dict(norm_cfg=norm_cfg)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
| 3,590 | 34.205882 | 79 |
py
|
ERD
|
ERD-main/configs/resnest/cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py
|
_base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=[
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
], ))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
| 3,394 | 35.117021 | 79 |
py
|
ERD
|
ERD-main/configs/resnest/cascade-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py
|
_base_ = './cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 257 | 31.25 | 74 |
py
|
ERD
|
ERD-main/configs/resnest/mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py
|
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg),
mask_head=dict(norm_cfg=norm_cfg)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
| 1,402 | 28.851064 | 79 |
py
|
ERD
|
ERD-main/configs/resnest/cascade-mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py
|
_base_ = './cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 256 | 31.125 | 73 |
py
|
ERD
|
ERD-main/configs/resnest/faster-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py
|
_base_ = './faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 256 | 31.125 | 73 |
py
|
ERD
|
ERD-main/configs/groie/mask-rcnn_r101_fpn_syncbn-r4-gcb_c3-c5-groie_1x_coco.py
|
_base_ = '../gcnet/mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2)),
mask_roi_extractor=dict(
type='GenericRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2))))
| 1,543 | 32.565217 | 78 |
py
|
ERD
|
ERD-main/configs/groie/mask-rcnn_r50_fpn_syncbn-r4-gcb-c3-c5-groie_1x_coco.py
|
_base_ = '../gcnet/mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2)),
mask_roi_extractor=dict(
type='GenericRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2))))
| 1,542 | 32.543478 | 78 |
py
|
ERD
|
ERD-main/configs/groie/grid-rcnn_r50_fpn_gn-head-groie_1x_coco.py
|
_base_ = '../grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2)),
grid_roi_extractor=dict(
type='GenericRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2))))
| 1,534 | 32.369565 | 78 |
py
|
ERD
|
ERD-main/configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py
|
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2))))
| 834 | 31.115385 | 77 |
py
|
ERD
|
ERD-main/configs/groie/mask-rcnn_r50_fpn_groie_1x_coco.py
|
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2)),
mask_roi_extractor=dict(
type='GenericRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
type='ConvModule',
in_channels=256,
out_channels=256,
kernel_size=5,
padding=2,
inplace=False,
),
post_cfg=dict(
type='GeneralizedAttention',
in_channels=256,
spatial_range=-1,
num_heads=6,
attention_type='0100',
kv_stride=2))))
| 1,526 | 32.195652 | 78 |
py
|
ERD
|
ERD-main/configs/albu_example/mask-rcnn_r50_fpn_albu-1x_coco.py
|
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.1, 0.3],
contrast_limit=[0.1, 0.3],
p=0.2),
dict(
type='OneOf',
transforms=[
dict(
type='RGBShift',
r_shift_limit=10,
g_shift_limit=10,
b_shift_limit=10,
p=1.0),
dict(
type='HueSaturationValue',
hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
p=1.0)
],
p=0.1),
dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
dict(type='ChannelShuffle', p=0.1),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.1),
]
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='Albu',
transforms=albu_train_transforms,
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_bboxes_labels', 'gt_ignore_flags'],
min_visibility=0.0,
filter_lost_elements=True),
keymap={
'img': 'image',
'gt_masks': 'masks',
'gt_bboxes': 'bboxes'
},
skip_img_without_anno=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
| 1,954 | 28.179104 | 76 |
py
|
ERD
|
ERD-main/configs/fsaf/fsaf_r50_fpn_1x_coco.py
|
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is implemented. The anchor generator only
# generates 1 anchor at each feature point, as a substitute of the
# grid of features.
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=1,
scales_per_octave=1,
ratios=[1.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(_delete_=True, type='TBLRBBoxCoder', normalizer=4.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0,
reduction='none'),
loss_bbox=dict(
_delete_=True,
type='IoULoss',
eps=1e-6,
loss_weight=1.0,
reduction='none')),
# training and testing settings
train_cfg=dict(
assigner=dict(
_delete_=True,
type='CenterRegionAssigner',
pos_scale=0.2,
neg_scale=0.2,
min_pos_iof=0.01),
allowed_border=-1,
pos_weight=-1,
debug=False))
optim_wrapper = dict(clip_grad=dict(max_norm=10, norm_type=2))
| 1,459 | 29.416667 | 77 |
py
|
ERD
|
ERD-main/configs/fsaf/fsaf_r101_fpn_1x_coco.py
|
_base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 192 | 26.571429 | 61 |
py
|
ERD
|
ERD-main/configs/fsaf/fsaf_x101-64x4d_fpn_1x_coco.py
|
_base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 414 | 26.666667 | 76 |
py
|
ERD
|
ERD-main/configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py
|
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='GridRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='GridRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
with_reg=False,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False),
grid_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
grid_head=dict(
type='GridHead',
grid_points=9,
num_convs=8,
in_channels=256,
point_feat_channels=64,
norm_cfg=dict(type='GN', num_groups=36),
loss_grid=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=15))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_radius=1,
pos_weight=-1,
max_num_grid=192,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.03,
nms=dict(type='nms', iou_threshold=0.3),
max_per_img=100)))
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
# training schedule
max_epochs = 25
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 80,
by_epoch=False,
begin=0,
end=3665),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[17, 23],
gamma=0.1)
]
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
| 5,068 | 30.484472 | 79 |
py
|
ERD
|
ERD-main/configs/grid_rcnn/grid-rcnn_x101-64x4d_fpn_gn-head_2x_coco.py
|
_base_ = './grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 380 | 26.214286 | 76 |
py
|
ERD
|
ERD-main/configs/grid_rcnn/grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py
|
_base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 373 | 25.714286 | 76 |
py
|
ERD
|
ERD-main/configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py
|
_base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
# training schedule
max_epochs = 12
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.0001, by_epoch=False, begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
| 414 | 19.75 | 70 |
py
|
ERD
|
ERD-main/configs/grid_rcnn/grid-rcnn_r101_fpn_gn-head_2x_coco.py
|
_base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 206 | 24.875 | 61 |
py
|
ERD
|
ERD-main/configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py
|
_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True)
# model settings
model = dict(
type='CornerNet',
data_preprocessor=data_preprocessor,
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, 2, 2, 2, 2, 4],
norm_cfg=dict(type='BN', requires_grad=True)),
neck=None,
bbox_head=dict(
type='CentripetalHead',
num_classes=80,
in_channels=256,
num_feat_levels=2,
corner_emb_channels=0,
loss_heatmap=dict(
type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1),
loss_guiding_shift=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
loss_centripetal_shift=dict(
type='SmoothL1Loss', beta=1.0, loss_weight=1)),
# training and testing settings
train_cfg=None,
test_cfg=dict(
corner_topk=100,
local_maximum_kernel=3,
distance_threshold=0.5,
score_thr=0.05,
max_per_img=100,
nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
# data settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
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(
# The cropped images are padded into squares during training,
# but may be smaller than crop_size.
type='RandomCenterCropPad',
crop_size=(511, 511),
ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
test_mode=False,
test_pad_mode=None,
mean=data_preprocessor['mean'],
std=data_preprocessor['std'],
# Image data is not converted to rgb.
to_rgb=data_preprocessor['bgr_to_rgb']),
dict(type='Resize', scale=(511, 511), keep_ratio=False),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs'),
]
test_pipeline = [
dict(
type='LoadImageFromFile',
to_float32=True,
backend_args=_base_.backend_args),
# don't need Resize
dict(
type='RandomCenterCropPad',
crop_size=None,
ratios=None,
border=None,
test_mode=True,
test_pad_mode=['logical_or', 127],
mean=data_preprocessor['mean'],
std=data_preprocessor['std'],
# Image data is not converted to rgb.
to_rgb=data_preprocessor['bgr_to_rgb']),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'border'))
]
train_dataloader = dict(
batch_size=6,
num_workers=3,
batch_sampler=None,
dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=0.0005),
clip_grad=dict(max_norm=35, norm_type=2))
max_epochs = 210
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 3,
by_epoch=False,
begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[190],
gamma=0.1)
]
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (16 GPUs) x (6 samples per GPU)
auto_scale_lr = dict(base_batch_size=96)
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(
nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'),
max_per_img=100))
tta_pipeline = [
dict(
type='LoadImageFromFile',
to_float32=True,
backend_args=_base_.backend_args),
dict(
type='TestTimeAug',
transforms=[
[
# ``RandomFlip`` must be placed before ``RandomCenterCropPad``,
# otherwise bounding box coordinates after flipping cannot be
# recovered correctly.
dict(type='RandomFlip', prob=1.),
dict(type='RandomFlip', prob=0.)
],
[
dict(
type='RandomCenterCropPad',
crop_size=None,
ratios=None,
border=None,
test_mode=True,
test_pad_mode=['logical_or', 127],
mean=data_preprocessor['mean'],
std=data_preprocessor['std'],
# Image data is not converted to rgb.
to_rgb=data_preprocessor['bgr_to_rgb'])
],
[dict(type='LoadAnnotations', with_bbox=True)],
[
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'flip', 'flip_direction', 'border'))
]
])
]
| 5,563 | 29.571429 | 79 |
py
|
ERD
|
ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.02-coco.py
|
_base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 2% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/[email protected]'
unlabeled_dataset.ann_file = 'semi_anns/[email protected]'
train_dataloader = dict(
dataset=dict(datasets=[labeled_dataset, unlabeled_dataset]))
| 445 | 43.6 | 79 |
py
|
ERD
|
ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py
|
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py',
'../_base_/datasets/semi_coco_detection.py'
]
detector = _base_.model
detector.data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32)
detector.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),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe'))
model = dict(
_delete_=True,
type='SoftTeacher',
detector=detector,
data_preprocessor=dict(
type='MultiBranchDataPreprocessor',
data_preprocessor=detector.data_preprocessor),
semi_train_cfg=dict(
freeze_teacher=True,
sup_weight=1.0,
unsup_weight=4.0,
pseudo_label_initial_score_thr=0.5,
rpn_pseudo_thr=0.9,
cls_pseudo_thr=0.9,
reg_pseudo_thr=0.02,
jitter_times=10,
jitter_scale=0.06,
min_pseudo_bbox_wh=(1e-2, 1e-2)),
semi_test_cfg=dict(predict_on='teacher'))
# 10% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/[email protected]'
unlabeled_dataset.ann_file = 'semi_anns/' \
'[email protected]'
unlabeled_dataset.data_prefix = dict(img='train2017/')
train_dataloader = dict(
dataset=dict(datasets=[labeled_dataset, unlabeled_dataset]))
# training schedule for 180k
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=180000, val_interval=5000)
val_cfg = dict(type='TeacherStudentValLoop')
test_cfg = dict(type='TestLoop')
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=180000,
by_epoch=False,
milestones=[120000, 160000],
gamma=0.1)
]
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=10000, max_keep_ckpts=2))
log_processor = dict(by_epoch=False)
custom_hooks = [dict(type='MeanTeacherHook')]
| 2,511 | 28.552941 | 79 |
py
|
ERD
|
ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.05-coco.py
|
_base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 5% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/[email protected]'
unlabeled_dataset.ann_file = 'semi_anns/[email protected]'
train_dataloader = dict(
dataset=dict(datasets=[labeled_dataset, unlabeled_dataset]))
| 445 | 43.6 | 79 |
py
|
ERD
|
ERD-main/configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.01-coco.py
|
_base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 1% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/[email protected]'
unlabeled_dataset.ann_file = 'semi_anns/[email protected]'
train_dataloader = dict(
dataset=dict(datasets=[labeled_dataset, unlabeled_dataset]))
| 445 | 43.6 | 79 |
py
|
ERD
|
ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages.py
|
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# Using 32 GPUS while training
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001),
clip_grad=dict(max_norm=35, norm_type=2))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 64,
by_epoch=False,
begin=0,
end=26000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
| 941 | 25.166667 | 75 |
py
|
ERD
|
ERD-main/configs/openimages/ssd300_32xb8-36e_openimages.py
|
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
bbox_head=dict(
num_classes=601,
anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
# dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
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={{_base_.model.data_preprocessor.mean}},
to_rgb={{_base_.model.data_preprocessor.bgr_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', scale=(input_size, input_size), keep_ratio=False),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
# avoid bboxes being resized
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'instances'))
]
train_dataloader = dict(
batch_size=8, # using 32 GPUS while training. total batch size is 32 x 8
batch_sampler=None,
dataset=dict(
_delete_=True,
type='RepeatDataset',
times=3, # repeat 3 times, total epochs are 12 x 3
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/oidv6-train-annotations-bbox.csv',
data_prefix=dict(img='OpenImages/train/'),
label_file='annotations/class-descriptions-boxable.csv',
hierarchy_file='annotations/bbox_labels_600_hierarchy.json',
meta_file='annotations/train-image-metas.pkl',
pipeline=train_pipeline)))
val_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.04, momentum=0.9, weight_decay=5e-4))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.001,
by_epoch=False,
begin=0,
end=20000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=256)
| 3,014 | 32.876404 | 79 |
py
|
ERD
|
ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py
|
_base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
train_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file='challenge2019/challenge-2019-train-detection-bbox.txt',
label_file='challenge2019/cls-label-description.csv',
hierarchy_file='challenge2019/class_label_tree.np',
meta_file='challenge2019/challenge-2019-train-metas.pkl'))
val_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file='challenge2019/challenge-2019-validation-detection-bbox.txt',
data_prefix=dict(img='OpenImages/'),
label_file='challenge2019/cls-label-description.csv',
hierarchy_file='challenge2019/class_label_tree.np',
meta_file='challenge2019/challenge-2019-validation-metas.pkl',
image_level_ann_file='challenge2019/challenge-2019-validation-'
'detection-human-imagelabels.csv'))
test_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file='challenge2019/challenge-2019-validation-detection-bbox.txt',
label_file='challenge2019/cls-label-description.csv',
hierarchy_file='challenge2019/class_label_tree.np',
meta_file='challenge2019/challenge-2019-validation-metas.pkl',
image_level_ann_file='challenge2019/challenge-2019-validation-'
'detection-human-imagelabels.csv'))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
| 1,712 | 41.825 | 78 |
py
|
ERD
|
ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages-challenge.py
|
_base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| 195 | 31.666667 | 78 |
py
|
ERD
|
ERD-main/configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages.py
|
_base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| 185 | 30 | 78 |
py
|
ERD
|
ERD-main/configs/openimages/retinanet_r50_fpn_32xb2-1x_openimages.py
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=601))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 64,
by_epoch=False,
begin=0,
end=26000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001),
clip_grad=dict(max_norm=35, norm_type=2))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
| 905 | 24.166667 | 75 |
py
|
ERD
|
ERD-main/configs/_base_/default_runtime.py
|
default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'),
)
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
| 759 | 29.4 | 76 |
py
|
ERD
|
ERD-main/configs/_base_/models/rpn_r50-caffe-c4.py
|
# model settings
model = dict(
type='RPN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
neck=None,
rpn_head=dict(
type='RPNHead',
in_channels=1024,
feat_channels=1024,
anchor_generator=dict(
type='AnchorGenerator',
scales=[2, 4, 8, 16, 32],
ratios=[0.5, 1.0, 2.0],
strides=[16]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=12000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0)))
| 1,980 | 29.476923 | 72 |
py
|
ERD
|
ERD-main/configs/_base_/models/retinanet_r50_fpn.py
|
# model settings
model = dict(
type='RetinaNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head=dict(
type='RetinaHead',
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=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
# model training and testing settings
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),
sampler=dict(
type='PseudoSampler'), # Focal loss should use PseudoSampler
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=0.5),
max_per_img=100))
| 2,059 | 28.855072 | 79 |
py
|
ERD
|
ERD-main/configs/_base_/models/faster-rcnn_r50-caffe-c4.py
|
# model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
rpn_head=dict(
type='RPNHead',
in_channels=1024,
feat_channels=1024,
anchor_generator=dict(
type='AnchorGenerator',
scales=[2, 4, 8, 16, 32],
ratios=[0.5, 1.0, 2.0],
strides=[16]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
shared_head=dict(
type='ResLayer',
depth=50,
stage=3,
stride=2,
dilation=1,
style='caffe',
norm_cfg=norm_cfg,
norm_eval=True,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=1024,
featmap_strides=[16]),
bbox_head=dict(
type='BBoxHead',
with_avg_pool=True,
roi_feat_size=7,
in_channels=2048,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=12000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=6000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
| 4,018 | 31.41129 | 78 |
py
|
ERD
|
ERD-main/configs/_base_/models/faster-rcnn_r50-caffe-dc5.py
|
# model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
out_indices=(3, ),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
rpn_head=dict(
type='RPNHead',
in_channels=2048,
feat_channels=2048,
anchor_generator=dict(
type='AnchorGenerator',
scales=[2, 4, 8, 16, 32],
ratios=[0.5, 1.0, 2.0],
strides=[16]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=2048,
featmap_strides=[16]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=2048,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=12000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms=dict(type='nms', iou_threshold=0.7),
nms_pre=6000,
max_per_img=1000,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
| 3,670 | 31.776786 | 77 |
py
|
ERD
|
ERD-main/configs/_base_/models/faster-rcnn_r50_fpn.py
|
# model settings
model = dict(
type='FasterRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
))
| 3,828 | 32.295652 | 79 |
py
|
ERD
|
ERD-main/configs/_base_/models/mask-rcnn_r50_fpn.py
|
# model settings
model = dict(
type='MaskRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=80,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)))
| 4,273 | 32.390625 | 79 |
py
|
ERD
|
ERD-main/configs/_base_/models/rpn_r50_fpn.py
|
# model settings
model = dict(
type='RPN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0)))
| 2,004 | 29.846154 | 79 |
py
|
ERD
|
ERD-main/configs/_base_/models/ssd300.py
|
# model settings
input_size = 300
model = dict(
type='SingleStageDetector',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
bgr_to_rgb=True,
pad_size_divisor=1),
backbone=dict(
type='SSDVGG',
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indices=(22, 34),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')),
neck=dict(
type='SSDNeck',
in_channels=(512, 1024),
out_channels=(512, 1024, 512, 256, 256, 256),
level_strides=(2, 2, 1, 1),
level_paddings=(1, 1, 0, 0),
l2_norm_scale=20),
bbox_head=dict(
type='SSDHead',
in_channels=(512, 1024, 512, 256, 256, 256),
num_classes=80,
anchor_generator=dict(
type='SSDAnchorGenerator',
scale_major=False,
input_size=input_size,
basesize_ratio_range=(0.15, 0.9),
strides=[8, 16, 32, 64, 100, 300],
ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2])),
# model training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.,
ignore_iof_thr=-1,
gt_max_assign_all=False),
sampler=dict(type='PseudoSampler'),
smoothl1_beta=1.,
allowed_border=-1,
pos_weight=-1,
neg_pos_ratio=3,
debug=False),
test_cfg=dict(
nms_pre=1000,
nms=dict(type='nms', iou_threshold=0.45),
min_bbox_size=0,
score_thr=0.02,
max_per_img=200))
cudnn_benchmark = True
| 1,959 | 29.625 | 71 |
py
|
ERD
|
ERD-main/configs/_base_/models/mask-rcnn_r50-caffe-c4.py
|
# model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_mask=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
rpn_head=dict(
type='RPNHead',
in_channels=1024,
feat_channels=1024,
anchor_generator=dict(
type='AnchorGenerator',
scales=[2, 4, 8, 16, 32],
ratios=[0.5, 1.0, 2.0],
strides=[16]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
shared_head=dict(
type='ResLayer',
depth=50,
stage=3,
stride=2,
dilation=1,
style='caffe',
norm_cfg=norm_cfg,
norm_eval=True),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=1024,
featmap_strides=[16]),
bbox_head=dict(
type='BBoxHead',
with_avg_pool=True,
roi_feat_size=7,
in_channels=2048,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
mask_roi_extractor=None,
mask_head=dict(
type='FCNMaskHead',
num_convs=0,
in_channels=2048,
conv_out_channels=256,
num_classes=80,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=12000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=14,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=6000,
nms=dict(type='nms', iou_threshold=0.7),
max_per_img=1000,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)))
| 4,275 | 31.150376 | 78 |
py
|
ERD
|
ERD-main/configs/_base_/models/cascade-mask-rcnn_r50_fpn.py
|
# model settings
model = dict(
type='CascadeRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='CascadeRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=80,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)))
| 7,169 | 34.147059 | 79 |
py
|
ERD
|
ERD-main/configs/_base_/models/cascade-rcnn_r50_fpn.py
|
# model settings
model = dict(
type='CascadeRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='CascadeRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
]),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
| 6,521 | 34.064516 | 79 |
py
|
ERD
|
ERD-main/configs/_base_/models/fast-rcnn_r50_fpn.py
|
# model settings
model = dict(
type='FastRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
| 2,256 | 31.710145 | 79 |
py
|
ERD
|
ERD-main/configs/_base_/schedules/schedule_20e.py
|
# training schedule for 20e
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=20,
by_epoch=True,
milestones=[16, 19],
gamma=0.1)
]
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
| 816 | 27.172414 | 79 |
py
|
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