Should the size of the photos be the same for deep learning?

Question

I have lots of image (about 40 GB). My images are small but they don't have same size. My images aren't from natural things because I made them from a signal so all pixels are important and I can't crop or delete any pixel.

Is it possible to use deep learning for this kind of images with different shapes?

All pixels are important, please take this into consideration. I want a model which does not depend on a fixed size input image. Is it possible?

Answer 1

Without knowing what you're trying to learn from the data, it's tough to give a definitive answer:

• You could pad all the data at the beginning (or end) of the signal so they're all the same size. This allows you to keep all the important pixels, but adds irrelevant information to the image that the network will most likely ignore.
• I've also had good luck with activations where you take a pretrained network and pull features from the image at a certain part of the network regardless of size (as long as it's larger than the network input size). Then run through a classifier. https://www.mathworks.com/help/deeplearning/ref/activations.html#d117e95083
• Or you could window your data, and only process smaller chunks at one time. https://www.mathworks.com/help/audio/examples/cocktail-party-source-separation-using-deep-learning-networks.html