In-House Models vs. Foundational Models?

Hey folks,

As I'm sure you know, there's a lot of buzz around Zero-Shot Foundational Models these days (e.g. SAM, OWL-ViT, ImageBind etc.), but not too much info on how these should be compared against "classic" in-house models (e.g. Yolo-v8/NAS).

Any thoughts on best-practices for comparing/evaluating these and deciding whether to integrate them into your CV pipeline?

P.S. Below is an example of an amusing edge case we found in a Zero-Shot Object Detection Model from HuggingFace, where it is …

computervision etc foundational models hey imagebind nas practices sam vit yolo