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- _base_ = './detr_r50_8xb2-150e_coco.py'
- # learning policy
- max_epochs = 500
- train_cfg = dict(
- type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=10)
- param_scheduler = [
- dict(
- type='MultiStepLR',
- begin=0,
- end=max_epochs,
- by_epoch=True,
- milestones=[334],
- gamma=0.1)
- ]
- # only keep latest 2 checkpoints
- default_hooks = dict(checkpoint=dict(max_keep_ckpts=2))
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (8 GPUs) x (2 samples per GPU)
- auto_scale_lr = dict(base_batch_size=16)
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