I am trying to understand a paragraph in the Faster RCNN paper.
We train and test both region proposal and object detection networks on single-scale images [7, 5]. We re-scale the images such that their shorter side is s = 600 pixels . Multi-scale feature extraction may improve accuracy but does not exhibit a good speed-accuracy trade-off . We also note that for ZF and VGG nets, the total stride on the last conv layer is 16 pixels on the re-scaled image, and thus is ∼10 pixels on a typical PASCAL image (∼500×375). Even such a large stride provides good results, though accuracy may be further improved with a smaller stride.
I would like to understand how they calculated the total stride on the last conv layer to be 16 pixels for the re-scaled image. I would like to decrease this stride to test accuracy improvements.
Could you please show me how to calculate the stride being 16 pixels on the re-scaled image?