Does the repo pick the weights that perform best in val dataset to evaluate in test dataset?

[pierowu]

Thank you for your solid work.
Does the repo implement the function that pick the model weights that perform best in val dataset to evaluate in test dataset?
From the code below, it seems that the repo directly choose the best results in test dataset as the final results?

PEViT/vision_benchmark/evaluation/lora_clip.py

Lines 284 to 291 in be6fb43

	acc1, logits = validate(test_dataloader, model, criterion, epoch, config, return_logits=True)
	# remember best acc@1 and save checkpoint
	if acc1 > best_acc1:
	model_info['best_logits'] = logits
	best_acc1 = max(acc1, best_acc1)
	logging.info(f'=> Learning rate {config.TRAIN.LR}, L2 lambda {config.TRAIN.WD}: Best score: Acc@1 {best_acc1:.3f}')

[jkooy]

Hi, thanks for the interests!
I just been notified you raised the same question in the elevater toolkit https://github.com/Computer-Vision-in-the-Wild/Elevater_Toolkit_IC. So basically the best results on the test set are reported. And the best weights are selected on the test set. This is the same setting used in the elevater toolkit for the fair comparison.

[pierowu]

Hi, thanks for the interests! I just been notified you raised the same question in the elevater toolkit https://github.com/Computer-Vision-in-the-Wild/Elevater_Toolkit_IC. So basically the best results on the test set are reported. And the best weights are selected on the test set. This is the same setting used in the elevater toolkit for the fair comparison.

It seems to bring some possibilities to overfit in the test set. However , since the elevater benchmark use this setting, maybe there is no better way but to follow it.