Image Classification on non real images I was wondering how image classifier networks perform on images that are not photographs. For example if you were to feed a drawing of a car or a face to an image classifier that was only trained on photos would the network still be able to classify the image correctly?

Furthermore what if you were to feed more and more abstract drawings into the network. As humans we are able to recognize objects even in abstract forms (i.e. modern art) but do current image classifiers generalize well enough in order to do this?

Are there any networks that are also trained on artist renditions of objects and not just photographs?
This question is slightly philosophical, but can be explained in this way - If you model is trained on real photographs it will likely not generalize well to things like drawings unless they are photorealistic or contain the features that the model is using to classify the images. You would likely have to include drawings in your training data to be able to do this effectively.

An interesting project leveraging Machine Learning that may have some relevance to what you are talking about is a recent Google project called "Quick Draw!" (link here) You should check it out. Essentially, it takes input of human drawings and outputs a classification label for what it thinks you drew.