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- _base_ = './yolof_r50-c5_8xb8-1x_coco.py'
- # We implemented the iter-based config according to the source code.
- # COCO dataset has 117266 images after filtering. We use 8 gpu and
- # 8 batch size training, so 22500 is equivalent to
- # 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch,
- # 20000 is equivalent to 10.9 epoch. Due to lr(0.12) is large,
- # the iter-based and epoch-based setting have about 0.2 difference on
- # the mAP evaluation value.
- train_cfg = dict(
- _delete_=True,
- type='IterBasedTrainLoop',
- max_iters=22500,
- val_interval=4500)
- # learning rate policy
- param_scheduler = [
- dict(
- type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
- dict(
- type='MultiStepLR',
- begin=0,
- end=22500,
- by_epoch=False,
- milestones=[15000, 20000],
- gamma=0.1)
- ]
- train_dataloader = dict(sampler=dict(type='InfiniteSampler'))
- default_hooks = dict(checkpoint=dict(by_epoch=False, interval=2500))
- log_processor = dict(by_epoch=False)
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