# ML techniques to classify simple polygons

I'm curious about suitability of Machine Learning techniques to classify grayscale images of polygons into categories defined by number of their sides, which will be kept small. Images will be "perfect" representations produced by an OpenGL rasterizer. Orientation, shape and scale of polygons will vary. Training data can be huge as it will be generated algorithmically on demand. In extreme case it can cover the whole domain, but the data size would get impractically large. Here is an image example, which belongs to category 4 (polygon with 4 sides):

• @ReneBt I elaborated a bit more what I'm trying to accomplish. In biological vision the eye and optic nerve is hardwired to do a lot of image processing that's what the NN (networks of artificial neurons) are advertised to imitate best, right? – Paul Jurczak Jun 13 '18 at 9:26