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- _base_ = './yolof_r50_c5_8x8_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.
- lr_config = dict(step=[15000, 20000])
- runner = dict(_delete_=True, type='IterBasedRunner', max_iters=22500)
- checkpoint_config = dict(interval=2500)
- evaluation = dict(interval=4500)
- log_config = dict(interval=20)
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