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bacteria_yolox_m_8x8_300e_coco.py
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bacteria_yolox_m_8x8_300e_coco.py
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optimizer = dict(
type='SGD',
lr=0.001,
momentum=0.9,
weight_decay=0.0005,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5,
num_last_epochs=15,
min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=1000)
checkpoint_config = dict(interval=50)
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [
dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
dict(type='SyncNormHook', num_last_epochs=15, interval=20, priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=None,
momentum=0.0001,
priority=49)
]
dist_params = dict(backend='nccl')
log_level = 'INFO'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=64)
img_scale = (640, 640)
model = dict(
type='YOLOX',
input_size=(640, 640),
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.67, widen_factor=0.75),
neck=dict(
type='YOLOXPAFPN',
in_channels=[192, 384, 768],
out_channels=192,
num_csp_blocks=2),
bbox_head=dict(
type='YOLOXHead', num_classes=8, in_channels=192, feat_channels=192),
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing'
train_coco_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_train_jbing.json'
test_coco_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_test_jbing.json'
val_coco_path = '/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_val_jbing.json'
CLASSES = ('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec')
train_pipeline = [
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine', scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(
type='Albu',
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, 0.5],
contrast_limit=[0.1, 0.5],
p=0.2),
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)
],
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_labels'],
min_visibility=0.0,
filter_lost_elements=True),
keymap=dict(img='image', gt_bboxes='bboxes'),
update_pad_shape=False,
skip_img_without_anno=True),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
train_dataset = dict(
type='MultiImageMixDataset',
dataset=dict(
type='CocoDataset',
ann_file=
'/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_train_jbing.json',
img_prefix='/home/badboy-002/github/senior_project/bacteria_img_jbing',
classes=('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False),
pipeline=[
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(
type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
])
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
persistent_workers=True,
train=dict(
type='MultiImageMixDataset',
dataset=dict(
type='CocoDataset',
ann_file=
'/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_train_jbing.json',
img_prefix=
'/home/badboy-002/github/senior_project/bacteria_img_jbing',
classes=('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False),
pipeline=[
dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-320, -320)),
dict(
type='MixUp',
img_scale=(640, 640),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(
type='FilterAnnotations',
min_gt_bbox_wh=(1, 1),
keep_empty=False),
dict(
type='Albu',
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, 0.5],
contrast_limit=[0.1, 0.5],
p=0.2),
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)
],
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_labels'],
min_visibility=0.0,
filter_lost_elements=True),
keymap=dict(img='image', gt_bboxes='bboxes'),
update_pad_shape=False,
skip_img_without_anno=True),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]),
val=dict(
type='CocoDataset',
ann_file=
'/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_val_jbing.json',
img_prefix='/home/badboy-002/github/senior_project/bacteria_img_jbing',
classes=('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='CocoDataset',
ann_file=
'/home/badboy-002/github/senior_project/bacteria_img_jbing/split_coco_jbing/merge_all_test_jbing.json',
img_prefix='/home/badboy-002/github/senior_project/bacteria_img_jbing',
classes=('unt', 'Amp', 'Cip', 'Rif', 'Tet', 'Col', 'Kan', 'Mec'),
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]))
max_epochs = 800
num_last_epochs = 15
interval = 20
evaluation = dict(
save_best='auto',
interval=1,
classwise=True,
dynamic_intervals=[(785, 1)],
metric='bbox')
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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, 0.5],
contrast_limit=[0.1, 0.5],
p=0.2),
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)
]
work_dir = './work_dirs/yolox_m_1-3-23'
auto_resume = False
gpu_ids = [0]