I have a dataset with roughly 800 images that are classified in 18 classes.
The classes are spread unevenly, with some classes having 30 images and others having 5.
In order to increase my dataset,I've decided to use image augmentation modifying each image a little,making 20 new images for every image.
I then decided to use my created images as my training set and my original images as my validation set.
Due to unavailability of a GPU,I couldn't train it a lot,but I ended up with around 50% success rate on a training set and 30% on the validation set.
Was the decision of only using my original dataset as validation a good one? If not,why?