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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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