There is a problem I've faced recently which I'm not sure my approach is proper or not. There is bunch of field videos which I run a semi-supervised detection model to extract crops to train my classifier. Until now everything seems fine. However the question/problem in here is that due to frame rate on detection model there is a lot of extracted crops that looks familiar (same or so tiny angle differences).
In my opinion it's not proper way to generate a dataset due to the lack of features on dataset so I remove some crops from dataset (lets say 10 crops with the same person and same/tiny different angle, I delete 8 of them)
Is that approach is proper or should I use all the data I collected regardless of whether the data is the same ?