SAM3 is cool - you can already do this more interactively on chat.vlm.run [1], and do much more. It's built on our new Orion [2] model; we've been able to integrate with SAM and several other computer-vision models in a truly composable manner. Video segmentation and tracking is also coming soon!

[1] https://chat.vlm.run

[2] https://vlm.run/orion

We (Roboflow) have had early access to this model for the past few weeks. It's really, really good. This feels like a seminal moment for computer vision. I think there's a real possibility this launch goes down in history as "the GPT Moment" for vision. The two areas I think this model is going to be transformative in the immediate term are for rapid prototyping and distillation.

Two years ago we released autodistill[1], an open source framework that uses large foundation models to create training data for training small realtime models. I'm convinced the idea was right, but too early; there wasn't a big model good enough to be worth distilling from back then. SAM3 is finally that model (and will be available in Autodistill today).

We are also taking a big bet on SAM3 and have built it into Roboflow as an integral part of the entire build and deploy pipeline[2], including a brand new product called Rapid[3], which reimagines the computer vision pipeline in a SAM3 world. It feels really magical to go from an unlabeled video to a fine-tuned realtime segmentation model with minimal human intervention in just a few minutes (and we rushed the release of our new SOTA realtime segmentation model[4] last week because it's the perfect lightweight complement to the large & powerful SAM3).

We also have a playground[5] up where you can play with the model and compare it to other VLMs.

[1] https://github.com/autodistill/autodistill

[2] https://blog.roboflow.com/sam3/

[3] https://rapid.roboflow.com

[4] https://github.com/roboflow/rf-detr

[5] https://playground.roboflow.com

I can't wait until it is easy to rotoscope / greenscreen / mask this stuff out accessibly for videos. I had tried Runway ML but it was... lacking, and the webui for fixing parts of it had similar issues.

I'm curious how this works for hair and transparent/translucent things. Probably not the best, but does not seem to be mentioned anywhere? Presumably it's just a straight line or vector rather than alpha etc?

Does the license allow for commercial purposes?

The 3D mesh generator is really cool too: https://ai.meta.com/sam3d/ It's not perfect, but it seems to handle occlusion very well (e.g. a person in a chair can be separated into a person mesh and a chair mesh) and it's very fast.

This model is incredibly impressive. Text is definitely the right modality, and now the ability to intertwine it with an LLM creates insane unlocks - my mind is already storming with ideas of projects that are now not only possible, but trivial.

Curious if anyone has done anything meaningful with SAM2 and streaming. SAM3 has built-in streaming support which is very exciting.

I’ve seen versions where people use an in-memory FS to write frames of stream with SAM2. Maybe that is good enough?

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/

This is an incredible model. But once again, we find an announcement for a new AI model with highly misleading graphs. That SA-Co Gold graph is particularly bad. Looks like I have another bad graph example for my introductory stats course...

With a avg latency of 4 seconds, this still couldn't be used in real-time video, correct?

First impressions are that this model is extremely good - the "zero-shot" text prompted detection is a huge step ahead of what we've seen before (both compared to older zero-shot detection models and to recent general purpose VLMs like Gemini and Qwen). With human supervision I think it's even at the point of being a useful teacher model.

I put together a YOLO tune for climbing hold detection a while back (trained on 10k labels) and this is 90% as good out of the box - just misses some foot chips and low contrast wood holds, and can't handle as many instances. It would've saved me a huge amount of manual annotation though.

Probably still can't get past a Google Captcha when on a VPN. Do I click the square with the shoe of the person who's riding the motorcycle?