The sliding window is of size (3,3) and the output of this sliding to the regression is 256 in case of ZF network.How do 9 Anchores with different scales and ratios implemented. I cant understand how these features with 9 different values are extracted and reduced to 256 and how the reference to the ground truth boxes is implemented ?Faster R-CNNUpdate In another paper that use the same concept as Region Proposal Network I can't understand how they did use the same size of pooling/box/conv but with different aspect ration. since they say the process on (5,5) with different aspect ratio. I'll share part of their work down. First, a canonical bounding box is centered on (x, y). The width and height of the box are the same as those of the conv filters (e.g., 55) in the subcategory conv layer, which have an aspect ratio one. Second, a number of boxes centered on (x, y) with the same areas as the canonical box (e.g., 25) but with different aspect ratios are generated. Finally, the RoI generating layer rescales the generated boxes according to  the scale of the heat map, so as to cover objects in different scales and aspect ratios.
How does RPN and Anchores work in faster R-CNN?
deep learning;convnet