All models were trained using the Adam optimizer with a custom learning rate scheduler. Sparse categorical cross entropy was used as a loss metric. The dropout rate was set to 0.1 to avoid overfitting. When training models using the images with the size of 512 x 512 pixels, the per-node batch size was set to 48. Training scripts and models are written in Python 3 with Keras and Tensorflow 2.8.0.