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Nhabls t1_iwc3rap wrote

Reply to comment by Tiny-Mud6713 in [P] Need help with this CNN transfer learning problem by Tiny-Mud6713

You don't augment validation data, you'd be corrupting your validation scores, you'd only augment it at the end when/if you're training with all the data

Speaking of, look at your class representation %s, accuracy might be completely misleading if you have 1 or 2 overwhelmingly represented classes

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Tiny-Mud6713 OP t1_iwc919l wrote

7 classes are equally distributed (500 images), only 1 has like 25% of the other data share (150-ish), it is a problem but I'm not sure how to solve it considering the fact that it's a challenge and I can't add data, augmentation will keep the imbalance since it augments everything equally.

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Nhabls t1_iwcdek4 wrote

The data doesn't seem that imbalanced, not to cause the issues you're having. And idk what you are using for augmentation but you can def augment classes to specifically solve imbalance ( i don't like doing that personally). My next guess would be looking at how you're splitting the data for train/val and/or freezing the vast majority of the pretrained model and maybe even just training on the last layer or 2 that you add on top.

Regardless, it's something that's useful to know (very frequent in real world datasets) here's a link that goes over how to weigh classes for such cases it's with tensorflow in mind but it's the same concept regardless

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