I would try without data augmentation first. You need a baseline to understand what helps and what doesn’t to increase performance. If there is a strong signal that can differentiate between the classes, 100 images may be enough. The amount of data you need is problem dependent it’s not a one size fits all. As others have said make sure youre splitting into train and test sets to evaluate performance and that each has a distribution of classes similar to the overall population (matters if you have an imbalanced dataset). Keep the network lightweight if you’re not using transfer learning and build it up from there. At a certain point it will overfit but it will most likely happen faster the larger your network is.