I could use some help with some heuristics for Machine Learning, like how much data do I need to make a workable model, what framework/approach makes more sense given my ultimate goals. Here's an example: there's a lot of ML tutorials on doing image identification. Like you have a series of images: picture one might have an apple and a pear in it, picture 2 might have an apple, orange, and a banana in it. Where I'm s…
Second, this could be more than enough! Especially if you are doing transfer learning.
Third, you can "inflate" the amount of images you have now with "Image Data Augmentation"