(1) Keras model was progressively trained from 256x256 to 512x512 size (due to limitations of Kaggle kernels upscaling to 1024x1024 was not feasible)

• 256x256, trained from zero for 70 epochs, batch_size=16 (no grad. accum.), init_lr=1e-3
• 512x512, initialize by 256x256 model weights, trained for 16 epochs (to fit in 9hrs training time on Kaggle kernel), batch_size=4, grad_accum=4, , init_lr=1e-3
• 512x512, initialize by previous 512x512 model weights, trained for 16 epochs, batch_size=4, grad_accum=4, init_lr=1e-4
• 512x512, initialize by previous 512x512 model weights, trained for 16 epochs, batch_size=4, grad_accum=4, init_lr=3e-5

(2) Pytorch model was progressively trained from the 512x512 image size to the 1024x1024 image size.

• 512x512, trained from the "imagenet" weights

  num_epochs = 50
  accumulation_steps = 2
  batch_size = 16
  learning_rate = 5e-4
  optimizer = Adam()
  scheduler = ReduceLROnPlateau(optimizer, mode="min", patience=3, verbose=True)
  loss = FocalLoss() + DiceLoss()
  

• 1024x1024, trained from the 512x512 weights

  num_epochs = 50
  accumulation_steps = 3
  batch_size = 10
  learning_rate = 5e-4
  optimizer = Adam()
  scheduler = ReduceLROnPlateau(optimizer, mode="min", patience=3, verbose=True)
  loss = FocalLoss() + DiceLoss()