I need to separate images into three categories: vectors, photos with vectors and pure photos. The classifying needs to happen in “real-time” so this leads me to my question: what would be good algorithm for classifying these types of images from accuracy/performance tradeoff perspective?
Images are not my speciality so all pointers are appreciated.
For performance heavy side of things I tried tensorflow with inception to get a baseline however the model reached only ~88% accuracy. I conclude this is due Inception being a photo classifying model and something like a solid color vector doesn’t really fit into its world.
There must be easier/more lightweight solution than deep learning to detect such a different type of images?
Source: Stack Overflow