[knicholes]

I apologize for such a naive comment, as I don't have experience in this field, but I've seen OpenAI do some pretty impressive image recognition tasks (multimodal LLMs). Have you tried uploading some images of successful injection castings and some of unsuccessful injection castings (they don't even have to be of the same mold), telling it "These are examples of success" "these are examples of failures, e.g. flashing, blemish, scratch, etc" and feeding it picture(s) of the casted object?

It'd be interesting to hear how effective that is.

[lasermatts]

LLMs like GPT-4o have some pretty impressive image performance. It can actually pick up some of the more obvious defects on our buckets (Steph tested it out just now).

Two problems though with the OpenAI approach: 1. You'd need a cloud connection to send those images up to and get the answer back down so that's cost in terms of your round-trip latency, network infra, and the OpenAI account itself.

2. It doesn't do well with the very subtle defects - mild shape changes, loss of features from short shots, etc

It might be worth using in the offline pipeline for auto labeling though!