Image classification with different number of training image for each class
Sometimes it could be the case that one class is simply more common than others. That really bears thinking about, usually. I would guess that is not really the case for what you describe (i.e. it's rather arbitrary what kind of things people would want to classify images of in production later), so let's ignore that case (although it may affect performance on a test set depending on what's the most common in the test set). In general though, you would try out some strategies (e.g. no oversampling whether some extent of oversampling with data augmentation) and test their performance on a realistic test set.

Definitely don't throw away any images. Normally we struggle to create realistic images in data augmentation - here you already have them! Thus, a much more appealing approach is to use a data generator that sample evenly (or in whatever proportions make the most sense) from each class to create batches of training data (with some data augmentation such as slight rotations, changes in colors/saturation etc.).