I'm trying to understand the role of data augmentation and how it can affect the performance/accuracy of a deep model. My target application is a fire classification (fire or not, on video frames), with almost 15K positive and negative samples, and I was using the following data augmentation techniques. Does using ALL the followings always increase the performance? Or we have to choose them somehow smartly given our target application?
rotation_range=20, width_shift_range=0.2, height_shift_range=0.2,zoom_range=0.2, horizontal_flip=True
When I think a bit more, fire is always straight up, so I think
shift might in fact worsen the results, given that it makes the image sides stretch like this, which is irrelevant to fires in video frames. Same with rotation. So I think maybe I should only keep
zoom_range=0.2, horizontal_flip=True and remove the first three. Because I see some false positives when we have a scene transition effect in videos.
Is my argument correct? Should I keep them or remove them?