# How is the stride calculated in the Faster RCNN paper?

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 [5]. Multi-scale feature extraction may improve accuracy but does not exhibit a good speed-accuracy trade-off [5]. 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.

I'm also looking at the prototxt for training and testing and I can't figure out how he says the stride is 16 pixels.

Could you please show me how to calculate the stride being 16 pixels on the re-scaled image?

There are 4 max pooling layers in VGG before the last convolutional layer, each which has stride 2. $$2^4 = 16$$, so moving over by one in the feature map corresponds to 16 pixels in the input.