Question about the training dataset

[rover5056]

Hi
I am a beginner at unsupervised learning and try to use swav on my own dataset. But there is a concept that makes me confused.

I was used to thinking that methods like swav do not need labeled images for the training process, it will run upon some none label images like KNN or K-MEANS cluster.

But when I run the main_swav.py file, the argument data_path needs a folder like ImageNet train. Is that mean I need to pre-label images and assign them to correspond dir to satisfy MultiCropDataset(datasets.ImageFolder). It looks like a classic supervised model training process...

Am I misunderstanding that parameter or the entire method?
Thanks for the reply and any help

[ggflow123]

Hi!
What I did is simply change the directory.
E.g. Change unlabeled -->( image1.png, image2.png, ...) to unlabeled --> temp_folder --> ( image1.png, image2.png, ...)