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yolox-s_8xb8-300e_coco-face.py
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300, val_interval=10)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='mmdet.QuadraticWarmupLR',
by_epoch=True,
begin=0,
end=5,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
eta_min=0.0005,
begin=5,
T_max=285,
end=285,
by_epoch=True,
convert_to_iter_based=True),
dict(type='ConstantLR', by_epoch=True, factor=1, begin=285, end=300)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(
type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True),
paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
auto_scale_lr = dict(enable=False, base_batch_size=64)
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=10, max_keep_ckpts=3),
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=[dict(type='LocalVisBackend')],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = 'https://download.openmmlab.com/mmdetection/' \
'v2.0/yolox/yolox_s_8x8_300e_coco/' \
'yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'
resume = False
img_scale = (640, 640)
model = dict(
type='YOLOX',
data_preprocessor=dict(
type='DetDataPreprocessor',
pad_size_divisor=32,
batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(480, 800),
size_divisor=32,
interval=10)
]),
backbone=dict(
type='CSPDarknet',
deepen_factor=0.33,
widen_factor=0.5,
out_indices=(2, 3, 4),
use_depthwise=False,
spp_kernal_sizes=(5, 9, 13),
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')),
neck=dict(
type='YOLOXPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
num_csp_blocks=1,
use_depthwise=False,
upsample_cfg=dict(scale_factor=2, mode='nearest'),
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')),
bbox_head=dict(
type='YOLOXHead',
num_classes=1,
in_channels=128,
feat_channels=128,
stacked_convs=2,
strides=(8, 16, 32),
use_depthwise=False,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish'),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0),
loss_bbox=dict(
type='IoULoss',
mode='square',
eps=1e-16,
reduction='sum',
loss_weight=5.0),
loss_obj=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0),
loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
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)))
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
backend_args = dict(backend='local')
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', prob=0.5),
dict(type='Resize', 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='PackDetInputs')
]
train_dataset = dict(
type='MultiImageMixDataset',
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
pipeline=[
dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_cfg=dict(filter_empty_gt=False, min_size=32)),
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', prob=0.5),
dict(type='Resize', 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='PackDetInputs')
])
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
dict(type='Resize', 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='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='MultiImageMixDataset',
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/coco_face_train.json',
data_prefix=dict(img='train2017/'),
pipeline=[
dict(
type='LoadImageFromFile',
backend_args=dict(backend='local')),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_cfg=dict(filter_empty_gt=False, min_size=32),
metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))),
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', prob=0.5),
dict(type='Resize', 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='PackDetInputs')
]))
val_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/coco_face_val.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
dict(type='Resize', 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='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))))
test_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/coco_face_val.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
dict(type='Resize', 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='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
metainfo=dict(CLASSES=('person', ), PALETTE=(220, 20, 60))))
val_evaluator = dict(
type='CocoMetric',
ann_file='data/coco/annotations/coco_face_val.json',
metric='bbox')
test_evaluator = dict(
type='CocoMetric',
ann_file='data/coco/annotations/instances_val2017.json',
metric='bbox')
max_epochs = 300
num_last_epochs = 15
interval = 10
base_lr = 0.01
custom_hooks = [
dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
dict(type='SyncNormHook', priority=48),
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0001,
strict_load=False,
update_buffers=True,
priority=49)
]
metainfo = dict(CLASSES=('person', ), PALETTE=(220, 20, 60))
launcher = 'pytorch'