While people usually tend to simply resize any image into a square while training a CNN (for example, resnet takes a 224x224 square image), that looks ugly to me, especially when the aspect ratio is not around 1.
(In fact, that might change ground truth, for example, the label that an expert might give the distorted image could be different than the original one).
So now I resize the image to, say, 224x160 , keeping the original ratio, and then I pad the image with 0s (by pasting it into a random location in a totally black 224x224 image).
My approach doesn't seem original to me, and yet I cannot find any information whatsoever about my approach versus the "usual" approach. Funky!
So, which approach is better? Why? (if the answer is data dependent, please share your thoughts regarding when one is preferable to the other.)
According to Jeremy Howard, padding a big piece of the image (64x160 pixels) will have the following effect: The CNN will have to learn that the black part of the image is not relevant and does not help distinguishing between the classes (in a classification setting), as there is no correlation between the pixels in the black part and belonging to a given class. As you are not hard coding this, the CNN will have to learn it by gradient descent, and this might probably take some epochs. For this reason, you can do it if you have lots of images and computational power, but if you are on a budget on any of them, resizing should work better.