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- _base_ = 'mmdet::rtmdet/rtmdet_l_8xb32-300e_coco.py'
- input_shape = 320
- model = dict(
- backbone=dict(
- deepen_factor=0.33,
- widen_factor=0.25,
- use_depthwise=True,
- ),
- neck=dict(
- in_channels=[64, 128, 256],
- out_channels=64,
- num_csp_blocks=1,
- use_depthwise=True,
- ),
- bbox_head=dict(
- in_channels=64,
- feat_channels=64,
- share_conv=False,
- exp_on_reg=False,
- use_depthwise=True,
- num_classes=1),
- 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_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='CachedMosaic',
- img_scale=(input_shape, input_shape),
- pad_val=114.0,
- max_cached_images=20,
- random_pop=False),
- dict(
- type='RandomResize',
- scale=(input_shape * 2, input_shape * 2),
- ratio_range=(0.5, 1.5),
- keep_ratio=True),
- dict(type='RandomCrop', crop_size=(input_shape, input_shape)),
- dict(type='YOLOXHSVRandomAug'),
- dict(type='RandomFlip', prob=0.5),
- dict(
- type='Pad',
- size=(input_shape, input_shape),
- pad_val=dict(img=(114, 114, 114))),
- dict(type='PackDetInputs')
- ]
- train_pipeline_stage2 = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='RandomResize',
- scale=(input_shape, input_shape),
- ratio_range=(0.5, 1.5),
- keep_ratio=True),
- dict(type='RandomCrop', crop_size=(input_shape, input_shape)),
- dict(type='YOLOXHSVRandomAug'),
- dict(type='RandomFlip', prob=0.5),
- dict(
- type='Pad',
- size=(input_shape, input_shape),
- pad_val=dict(img=(114, 114, 114))),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='Resize', scale=(input_shape, input_shape), keep_ratio=True),
- dict(
- type='Pad',
- size=(input_shape, input_shape),
- pad_val=dict(img=(114, 114, 114))),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- train_dataloader = dict(
- dataset=dict(pipeline=train_pipeline, metainfo=dict(classes=('person', ))))
- val_dataloader = dict(
- dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=('person', ))))
- test_dataloader = val_dataloader
- custom_hooks = [
- dict(
- type='EMAHook',
- ema_type='ExpMomentumEMA',
- momentum=0.0002,
- update_buffers=True,
- priority=49),
- dict(
- type='PipelineSwitchHook',
- switch_epoch=280,
- switch_pipeline=train_pipeline_stage2)
- ]
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