I think it highly depends on the specific task.
I mean if you say you have less then 1% of the images for one class it could be still enough. For example 0.5% of images in one class would still be 1200 images per class which is quiet okay.
Also there might be classes which are pretty similiar to each other, so it will be harder and maybe more images are needed to distinct these to classes, while other classes are easy to seperate.
If you ask for methods to overcome this problem there are several.
One for example is over- or undersampling. So drop images of classes with higher percentage in dataset, or duplicate images of classes with lower percentage. This is to get all classes into a balanced ratio.
Also agressive augmentation could be used, e.g. flipping, rotating, contrast adaption, shearing, zooming, etc..., just remember: do not destroy the semantic of images.
If you want to test the classifier performance you can still use validation accuracy, but you have to build your validation set with balanced number of classes and these images should be representative for each class! Also the sample size in the validation set must be high enough.