Hey Anurag,
Creating a simpler version of your dataset for quick prototyping isn’t really something that can always be approached the same way - I’d say for classification, if you have a dataset that’s too large for prototyping then either you have way more samples per class than classes and then you take fewer samples, or you have not that many samples per class but many classes (such as your case).
If I were you, I would just select some subset of classes (5-10), maybe incorporating some domain knowledge so that the classes aren’t too easy, and prototype on that, and then move on to the full dataset once I am done prototyping and would like to run on the whole thing. I wouldn’t reduce the number of samples per class or the resolution of the images because that can affect stability of training and generalization to the full dataset.
Taking a subset of classes is fairly easy - you just look for the indices where y is in your list of classes and then you only keep the x and y at those indices.
Also feel free to share your code if you’d like 