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So i've been reading a lot about neural network for face detection, and all of the paper i've read suggested a neural network with an input layer of 20x20 image and 3 hidden types of hidden units:
4 which look at 10x10 pixel subregions,
16 which look at 5x5 pixel subregions,
and 6 which look at overlapping 20x5 pixel horizontal stripes of pixels.
something like this

I want to know why this structure is used, can anyone explain this in detail?
I'll be very grateful for any answer.

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