For example let’s say I want to classify eggs. And eggs in images can often be seen as just an egg, eggs in an egg cartoon and the carton can be closed or opened.

A naive approach would be to put all these image under the class eggs. But it might work better if there are 2 classes one for eggs and one for eggs in carton so training should be easier since these can look quite different since a group of eggs looks much different from a closed carton of eggs. I also feel like separating the classes can have unwanted outcomes like separating contexts. For example eggs withing cartons could rely on context of being in a kitchen and grocery store so it may less accurately predict an image has eggs if it is a carton of eggs in a farm.

Is my thinking correct on this?

What has been your experience with similar situations?

This question specifically focuses on image classification using neural nets.