LR optimizations
There are a few parameter updates we can apply to improve both the accuracy and the speed of our training. This can be achieved by increasing the batch size and tuning the LR. Another common method is to apply warmup and gradually increase our learning rate. This is beneficial especially when we use very high learning rates and helps with the stability of the training in the early epochs. Finally, another optimization is to apply Cosine Schedule to adjust our LR during the epochs. A big advantage of cosine is that there are no hyper-parameters to optimize, which cuts down our search space.
Here are the additional optimizations applied on top of the baseline recipe. Note that we’ve run multiple experiments to determine the optimal configuration of the parameters:
  batch_size=128,  # per GPU

  lr=0.5, 
  lr_scheduler='cosineannealinglr', 
  lr_warmup_epochs=5, 
  lr_warmup_method='linear', 
  lr_warmup_decay=0.01,
