Hello everyone. I have a conceptual question. I’m trying to understand the working of Faster and Mask RCNN. I’m working with two sizes of images (RGB), 1280x720 and 720x720, for two different tasks. These networks first’s block is the GeneralizedRCNNTransform module, that does normalization and resizing. The resize is defined by the parameters min_size and max_size. When I use batches of images of 1280x720, I keep the default values min_size=800 and max_size=1333, on the other hand I use min_size=800, max_size = 800 when I’m using images of 720x720. Checking the outcome of this module, I get images of 1344x768 and 800x800 respectively, and in both cases the network accepts these batches. It’s really weird to me that the network can process images of different sizes, and aspect ratios, because several CNN’s in other implementations need images of fixed sizes. My question is; How does the network do it? Is it because the network uses proposals regions and doesn’t need to flatten the output of the whole tensor of features? Or is something about the resizing that I’m missing?