How far away is ML from identifying everything in a picture?
For example if we could take a photo of Noah's Ark loading up every animal?
Do you just loop through each NN you have on each species?
A single NN can predict more than one class of object. The ImageNet competition has 20,000 classes.
There's also image segmentation as another poster has pointed to.
In the case of FB face tagging, they'd have learn an embedding space for faces, and when a new image comes in they'd place it in the embedding space along with all the person's connections and find the nearest neighbors.
See https://arxiv.org/abs/1503.03832 or the implementation https://cmusatyalab.github.io/openface/
This seems to be what you are looking for: https://code.facebook.com/posts/561187904071636/segmenting-a...
These are way better than anything a non-expert human can do. For example, it can distinguish between the Rhinoceros Auklet and the Parakeet Auklet.
I'm not sure what expert performance is, but around 94% is where humans top out on most tasks.
Also, the parent poster knows what they are talking about: https://www.semanticscholar.org/author/Stephen-Merity/337544...