[sneilan1]

Have you found any models that work better for your use case?

[bahmboo]

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

[grumbelbart2]

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.