Open-source Lead @roboflow. VLMs. GPU poor. Dog person. Coffee addict. Dyslexic. | GH: https://t.co/dEmzMDGXVf | HF: https://t.co/4Lx1Yw3CLF
Shorts
there's no catch; SAM3 is open source and really good one of the things it does really well is object tracking, even in crazy complex scenes like basketball probably my favorite computer vision model ever
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looks like we are working on the same thing haha; more soon
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basketball AI (95% local AI + 5% GPT-6 Astra) - detect ball and players - track players - re-identify players across plays - OCR player numbers - recognize player in possession - detect court keypoints - map player positions and trajectories Astra is powerful, but expensive tool. use it when it actually makes a difference. ↓ deep dive
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I can finally map NBA player's position from the camera perspective onto the court map it's still a bit shaky... I'll smooth it out later it's time to detect shooting motions and mark the shot location! some of the code has already been migrated to:
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SAM3 video tracking is so good yesterday: collect data, train custom object detector, use tracker to estimate object motion - days today: track anything with text prompt - seconds
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I spend a lot of time finding out what VLMs still can't do pretty excited to help change that we launched Roboflow Labs at Roboflow training data, evals and environments for real-world tasks. vision, multimodal reasoning, robotics and coding. link:
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I used to hate working on projects like this... crazy how the word has changed over the past year; labeling is dead!
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this might be the coolest-looking football AI visualization I ever created
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supervision - a computer vision library I created - just crossed 15,000 stars on GitHub! BBBRRRRRR! link:
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I'm starting to get more and more serious with YOLO-World; trying to solve real-life problems. I wanted to see if YOLO-World could recognize that the holes had been filled out. It was pretty tricky, but I learned a little about prompting. ↓ read more
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RF-DETR is now available in Hugging Face transformers state of the art in both detection and segmentation, outperforming YOLO architectures - checkpoints: - demo: - docs:
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SAM 3D Body is a CVPR 2026 award candidate paper from AI at Meta model recovers a full 3D human body mesh from a single RGB image you can run it automatically, or guide the reconstruction with masks and 2D keypoints thx to Niels Rogge for awesome demo idea
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pip install trackers trackers v2.1.0 is out; this release adds support for ByteTrack, a fast tracking by detection algorithm focused on stable identities under occlusion. link:
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I'm taking my football/soccer project to the next level today, I worked on detecting players, referees, and the ball and mapping their positions from video frames to positions on the field. ↓ read more
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"can you do the same thing for football?" - I got this question 100x today yep, I actually did this last year - player detection and tracking - team clustering - perspective transformation end-to-end YT tutorial:
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INSID3 segments objects across domains using ONLY ONE annotated example it works entirely without a segmentation decoder, task-specific fine-tuning, or external mask generators like SAM CVPR 2026 paper with enormous practical potential
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almost fully functional version of my football AI project today, I added player tracking using ByteTrack and projection of players onto the map code coming soon:
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supervision-0.26.0 is out we finally released support for ViTPose and ViTPose++ pose estimation models from Hugging Face transformers link: