mirror of https://github.com/open-mmlab/mmyolo.git
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
_base_ = './yolov6_s_syncbn_fast_8xb32-400e_coco.py'
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data_root = './data/cat/'
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class_name = ('cat', )
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num_classes = len(class_name)
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metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
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max_epochs = 40
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train_batch_size_per_gpu = 12
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train_num_workers = 4
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num_last_epochs = 5
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load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035-932e1d91.pth' # noqa
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model = dict(
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backbone=dict(frozen_stages=4),
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bbox_head=dict(head_module=dict(num_classes=num_classes)),
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train_cfg=dict(
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initial_assigner=dict(num_classes=num_classes),
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assigner=dict(num_classes=num_classes)))
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train_dataloader = dict(
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batch_size=train_batch_size_per_gpu,
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num_workers=train_num_workers,
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dataset=dict(
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data_root=data_root,
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metainfo=metainfo,
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ann_file='annotations/trainval.json',
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data_prefix=dict(img='images/')))
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val_dataloader = dict(
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dataset=dict(
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metainfo=metainfo,
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data_root=data_root,
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ann_file='annotations/test.json',
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data_prefix=dict(img='images/')))
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test_dataloader = val_dataloader
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val_evaluator = dict(ann_file=data_root + 'annotations/test.json')
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test_evaluator = val_evaluator
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_base_.optim_wrapper.optimizer.batch_size_per_gpu = train_batch_size_per_gpu
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_base_.custom_hooks[1].switch_epoch = max_epochs - num_last_epochs
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default_hooks = dict(
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checkpoint=dict(interval=10, max_keep_ckpts=2, save_best='auto'),
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# The warmup_mim_iter parameter is critical.
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# The default value is 1000 which is not suitable for cat datasets.
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param_scheduler=dict(max_epochs=max_epochs, warmup_mim_iter=10),
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logger=dict(type='LoggerHook', interval=5))
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train_cfg = dict(
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max_epochs=max_epochs,
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val_interval=10,
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dynamic_intervals=[(max_epochs - num_last_epochs, 1)])
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# visualizer = dict(vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]) # noqa
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