I was going over the cifar 10 tutorial in tensorflow and was trying to understand why the guys in tensorflow/google decided to crop the images. The only reason I could justify it to myself is because they wanted to possibly decrease the computation time when training the neural net, otherwise it seems rather random.
I've talked with colleges and they imply it has to do with data set augmentation, however, data set augmentation can be done, regardless the size of the original image (to my understanding).
My initial guess of why they might be cropping and then augmenting the data set (with flips, brightness changes and contrasts) is because they want to get the actual image object and ignore the surroundings and then apply the transformation. If that were the goal, then for me what would have made sense is to not apply a random crop as they are doing (otherwise how do you know you are getting the actual object?) and then do the data set augmentation. Since thats not whats happening, I have my doubts, that my guess is correct.
Can someone clarify why the cropping is being done? Maybe I am just overthinking it but it would be great to clarify whats going on. For me it seems its just some (arbitrary) preprocessing that they are doing god knows why.