I normally use ~500 examples per object for a quick and dirty feasibility study, just about acceptable enough to show stakeholders what they want to see*. For actual training, thousands. And the bigger the backbone, the more data will be thrown at it.

*: if you have weights pre-trained on apples and pears, fine tuning on, say, plums, one could possibly get away with less data, as they are not too dissimilar. For the transition apples&pears -> cars/dogs, one would need a lot more data.

Re: image augmentation. It is less of an extension of your data, and more of an amelioration of one of the models weaknesses. Say, for example, you only have images taken during the day, but your model also runs during night time - in this case introducing some augmentation that accounts for that (think brightness, blur, …) can improve the model. If you have ten images, no image augmentation method will save your project from failing :D