When training an image classifier, is it best practice to equally distribute the number of images in each category? When training the model, I understand


If I supply too many on a certain category, it may become overfitted and treat almost all predictions as the overfitted category. This can lead to false positives in the overfitted directory.
If instead, I supply the least amount of photos to equally distribute, is the probability of of a rarer category appearing is now equal to a category that would be common to find? This can lead to false positives in the rare category but may turn my false positives in #1 to be true negatives.
If instead I skip training the rare category, I get false positives in other categories.


How do we account for the natural distribution of our target classifications (for example, types of common lesion vs rare lesions)?

Is it best practice to equally distribute? If so, what should we do if we have so little of the rare category, the sample images are orders of magnitude smaller?
Depending on the method that you use, this answer might be different.

First, you split the data between training, validation, and test splits. Forget about the test set. Keep the validation data in its natural distribution of classes.

You have several choices with the training set data -


Balance the classes using down-sampling or up-sampling techniques
Keep imbalanced classes but attach weights to errors in each class (See som references here: 
https://stats.stackexchange.com/questions/207977/balancing-classes-for-neural-network-training)