The problem I am trying to solve is, given two images, determining whether they contain the same object or not. Here is an example:
The first two images contain the same object, while the third image contains a similar, but different object. My goal is for the first two images to be seen as a match, but the first and third (and second and third) being seen as not matching. I want the matching to work in general with any object. It should be able to tell if any two pictures of any two objects are identical objects (not just similar) even if they are taken at different angles, cameras, and lighting conditions.
To accomplish this goal I have moved towards using transfer learning to train a siamese neural network (on a pretrained imagenet model) with the triplet loss function. The problem is that I do not have a dataset that is suitable for this. I cannot find any datasets that have pairs of identical objects from different angles, lighting, cameras, ... This is what I believe I would need for training to be successful.