Evaluation in classifier training is using supervised segmentation maps?

[lucasdavid]

Hello, thank you for the great repository! It's pretty impressive how organized it is.

I have a critic (or maybe a question, in case I got it wrong) regarding the training of the classifier, though:
I understand the importance of measuring and logging the mIoU during training (specially when creating the ablation section in your paper), however it doesn't strike me as correct to save the model with best mIoU. This procedural decision is based on fully supervised segmentation information, which should not be available for a truly weakly supervised problem; while resulting in a model better suited for segmentation.
The paper doesn't address this. Am I right to assume all models were trained like this? Were there any trainings where other metrics were considered when saving the model (e.g. classification loss or Eq (7) in the paper)?