There is no real limit for the size of training data. It does depend on the number of layers, but as long as you have more samples(say in the range 10k-100k, I have trained with even 500-1k samples) with diverse augmentations, it should be okay.
Also it depends on your usecase. For some applications, you may not find enough samples in which case you have to go with more augmentations.