How many images do you need to train a neural network?

Today I got an email with a question I've heard many times – "How many images do I need to train my classifier?". In the early days I would reply with the technically most correct, but also useless answer of "it depends", but over the last couple of years I've realized that just having a very approximate rule of thumb is useful, so here it is for posterity: You need 1,000 representative images for each class. Like all models, this rule is wrong but sometimes useful. In the rest of this post I'll cover where it came from, why it's wrong, and what it's still good for. The origin of the 1,000-image magic number comes from the original ImageNet classification challenge, where the dataset had 1,000 categories, each with a bit less than 1,000 images for each class (most I looked at had around seven or eight hundred).