Unfair comparions? : Saving the best model during training, Kfold experimneting and Image sizes

[aminpdik]

Hi @Seokju-Cho

Thank you for the code.

I have several questions that would be great If you answer.

As I see in the code, you are saving the best model by validating on validation data of the current fold(Pascal dataset) in every epoch of training. Am I right? In pascal dataset validation data are used for reporting the MIOU and should not be used during training.

For reporting the model accuracy, most of the papers in few-shot segmentation are using Kfold experimenting. As I see in your code you report the accuracy just by one try. Is it right?

The final question is image sizes for training and validation. As I see in your code, you are using 473 which is different than HSnet (400) and REPRI (417) net. As it is mentioned in Hsnet GitHub issues " juhongm999/hsnet#6 ", The author claimed, "In our experiments, we found that larger image sizes typically (> 417) result in better mIoU results". You are better than HSnet by a small margin which can be due to image sizes. Am I right?

Best