As someone that works on a platform users have used for labeling 1B images, I'm bullish SAM 3 can automate at least 90% of the work. Data prep is flipped to models being human-assisted instead of humans being model-assisted (see "autolabel" https://blog.roboflow.com/sam3/). I'm optimistic majority of users can now start deploying a model to then curate data instead of the inverse.
A brief history. SAM 1 - Visual prompt to create pixel-perfect masks in an image. No video. No class names. No open vocabulary. SAM 2 - Visual prompting for tracking on images and video. No open vocab. SAM 3 - Open vocab concept segmentation on images and video.
Roboflow has been long on zero / few shot concept segmentation. We've opened up a research preview exploring a SAM 3 native direction for creating your own model: https://rapid.roboflow.com/
Yes. But also note that redistribution of SAM 3 requires using the same SAM 3 license downstream. So libraries that attempt to, e.g., relicense the model as AGPL are non-compliant.
The bike lane compliant vehicle category is exciting. Infinite Machine (infinitemachine.com) made me aware of this category with their Olto model, which is at a (surprisingly) superior price point.
One of the most common uses for edge AI not listed in this course is computer vision. You similarly want real-time inference for processing video. Another open source project that makes it easy to use SOTA vision models on the edge is inference: https://github.com/roboflow/inference
Reminds me of NY Cerebro, semantic search across New York City's hundreds of public street cameras: https://nycerebro.vercel.app/ (e.g. search for "scaffolding")
both the endeavor and the site are super cool - congrats on 10 years. interaction on the graphics would be a nice touch to select into a specific run. went looking for the code on your GH! https://github.com/friggeri
