Show HN: How to Notebooks for computer vision models and techniques

Getting started with new and state-of-the-art vision models is often daunting. Documentation can be hard to parse, it can take a while to figure out how to run inference on an image.

We (the Roboflow open source team) actively write open source Google Colab notebooks showing how to use new SOTA models. Our library covers SAM, CLIP, Detectron2, YOLOv8, RTMDet, DINOv2, and more.

We also have code on common vision patterns like counting objects in a zone, vector analysis, object tracking with ByteTrack, and more.

I often turn to these notebooks as a quickstart both at work and for side projects when working with new model architectures: I can take the code I need and get to work solving a problem.