To answer your question: no but we haven't looked because Sam is sota. Trained our own model with limited success (I'm no expert). We are pursuing a classical computer vision approach. At some level segmenting a monochrome image resembles or is actually an old fashioned flood fill - very generally. This fantastic sam model is maybe not the right fit for our application.

Edit: answered the question

This is a "classic" machine vision task that has traditionally been solved with non-learning algorithms. (That in part enabled the large volume, zero defect productions in electronics we have today.) There are several off-the-shelf commercial MV tools for that.

Deep Learning-based methods will absolutely have a place in this in the future, but today's machines are usually classic methods. Advantages are that the hardware is much cheaper and requires less electric and thermal management. This changes these days with cheaper NPUs, but with machine lifetimes measured in decades, it will take a while.

Way late response: the off the shelf stuff is very very expensive as one would expect for industrial solutions. I was tasked to build something from scratch (our own solution). It was quite the journey and was not successful. If anyone has pointers or tips in this department I would truly love to hear about them!

My initial thought on hearing about this was it being used for learning. It would be cool to be able to talk to an LLM about how a circuit works, what the different components are, etc.