This has been my experience

Could DINO or some other model be used to identify fillable form fields in webforms and/or PDF forms and/or desktop apps? Or does it likely just work on real world photos and cartoons and stuff?

> The only way this trend reverses, is if compute becomes so cheap and ubiquitous, that everyone can achieve the necessary scale. We would still need the 100 M+ images with accurate labels. That work can be performed collectively and open sourced but it must be maintained etc. I don't think it will be easy.

Just 4 images?! Damn. I’ve had to do at least in the 100’s. I guess it depends on the complexity of the segmentation.

This was exactly my experience being the ML engineer on a predictive maintenance project. We detected broken traffic signs in video feeds from trucks; first you segment, then you classify. Simply yeeting every "object of interest" into DINOv2 and running any cheap classifier on that was a game changer.

I haven't compared image models in a long while, so I don't know the relevant performance metrics. But even a few years ago, you would usually use a pretrained model, and then finetune on your own dataset though. So those models would also have "seen millions of images", and not just your 100k. This change of not needing ML engineers is not so much about the models, as it is about easy API access for how to finetune a model, it seems to me? Of course it's great that the models have advanced and become better, and more robust though.

Things like DINO, GroundingDINO, SAM (and whatever the latest versions of those are) are incredible. I think the progress in this field has been overlooked given LLMs, they're less end-user friendly but they're so good compared to what I remember working with. I was able to turn around a segmentation and classifier demo in almost no time because they gave me fast and quick segmentation from a text description and then I trained a YOLO model on the results.