Created
February 11, 2022 11:15
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YOLOX-s config for DeepPCB dataset
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_base_ = ['configs/_base_/schedules/schedule_1x.py', 'configs/_base_/default_runtime.py'] | |
img_scale = (640, 640) | |
classes = ('open', 'short', 'mousebite', 'spur', 'copper', 'pin-hole') | |
# model settings | |
model = dict( | |
type='YOLOX', | |
input_size=img_scale, | |
random_size_range=(15, 25), | |
random_size_interval=10, | |
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5), | |
neck=dict( | |
type='YOLOXPAFPN', | |
in_channels=[128, 256, 512], | |
out_channels=128, | |
num_csp_blocks=1), | |
bbox_head=dict( | |
type='YOLOXHead', num_classes=6, in_channels=128, feat_channels=128), | |
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)), | |
# In order to align the source code, the threshold of the val phase is | |
# 0.01, and the threshold of the test phase is 0.001. | |
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65))) | |
# dataset settings | |
data_root = '' | |
dataset_type = 'CocoDataset' | |
train_pipeline = [ | |
dict(type='Mosaic', img_scale=img_scale, pad_val=114.0), | |
dict( | |
type='RandomAffine', | |
scaling_ratio_range=(0.1, 2), | |
border=(-img_scale[0] // 2, -img_scale[1] // 2)), | |
dict( | |
type='MixUp', | |
img_scale=img_scale, | |
ratio_range=(0.8, 1.6), | |
pad_val=114.0), | |
dict(type='YOLOXHSVRandomAug'), | |
dict(type='RandomFlip', flip_ratio=0.5), | |
# According to the official implementation, multi-scale | |
# training is not considered here but in the | |
# 'mmdet/models/detectors/yolox.py'. | |
dict(type='Resize', img_scale=img_scale, keep_ratio=True), | |
dict( | |
type='Pad', | |
pad_to_square=True, | |
# If the image is three-channel, the pad value needs | |
# to be set separately for each channel. | |
pad_val=dict(img=(114.0, 114.0, 114.0))), | |
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False), | |
dict(type='DefaultFormatBundle'), | |
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | |
] | |
train_dataset = dict( | |
type='MultiImageMixDataset', | |
dataset=dict( | |
type=dataset_type, | |
classes=classes, | |
ann_file='train.json', | |
img_prefix='', | |
pipeline=[ | |
dict(type='LoadImageFromFile'), | |
dict(type='LoadAnnotations', with_bbox=True) | |
], | |
filter_empty_gt=False, | |
), | |
pipeline=train_pipeline) | |
test_pipeline = [ | |
dict(type='LoadImageFromFile'), | |
dict( | |
type='MultiScaleFlipAug', | |
img_scale=img_scale, | |
flip=False, | |
transforms=[ | |
dict(type='Resize', keep_ratio=True), | |
dict(type='RandomFlip'), | |
dict( | |
type='Pad', | |
pad_to_square=True, | |
pad_val=dict(img=(114.0, 114.0, 114.0))), | |
dict(type='DefaultFormatBundle'), | |
dict(type='Collect', keys=['img']) | |
]) | |
] | |
data = dict( | |
samples_per_gpu=8, | |
workers_per_gpu=4, | |
persistent_workers=True, | |
train=train_dataset, | |
val=dict( | |
type=dataset_type, | |
classes=classes, | |
ann_file='test.json', | |
img_prefix='', | |
pipeline=test_pipeline), | |
test=dict( | |
type=dataset_type, | |
classes=classes, | |
ann_file='test.json', | |
img_prefix='', | |
pipeline=test_pipeline)) | |
# optimizer | |
# default 8 gpu | |
optimizer = dict( | |
type='SGD', | |
lr=0.01, | |
momentum=0.9, | |
weight_decay=5e-4, | |
nesterov=True, | |
paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.)) | |
optimizer_config = dict(grad_clip=None) | |
max_epochs = 20 | |
num_last_epochs = 5 | |
resume_from = None | |
interval = 5 | |
# learning policy | |
lr_config = dict( | |
_delete_=True, | |
policy='YOLOX', | |
warmup='exp', | |
by_epoch=False, | |
warmup_by_epoch=True, | |
warmup_ratio=1, | |
warmup_iters=5, # 5 epoch | |
num_last_epochs=num_last_epochs, | |
min_lr_ratio=0.05) | |
runner = dict(type='EpochBasedRunner', max_epochs=max_epochs) | |
custom_hooks = [ | |
dict( | |
type='YOLOXModeSwitchHook', | |
num_last_epochs=num_last_epochs, | |
priority=48), | |
dict( | |
type='SyncNormHook', | |
num_last_epochs=num_last_epochs, | |
interval=interval, | |
priority=48), | |
dict( | |
type='ExpMomentumEMAHook', | |
resume_from=resume_from, | |
momentum=0.0001, | |
priority=49) | |
] | |
checkpoint_config = dict(interval=interval) | |
evaluation = dict( | |
save_best='auto', | |
# The evaluation interval is 'interval' when running epoch is | |
# less than ‘max_epochs - num_last_epochs’. | |
# The evaluation interval is 1 when running epoch is greater than | |
# or equal to ‘max_epochs - num_last_epochs’. | |
interval=interval, | |
dynamic_intervals=[(max_epochs - num_last_epochs, 1)], | |
metric='bbox') | |
log_config = dict(interval=50) |
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