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volleyball machine learning model versus anime same workflow using roboflow rapid + SAM3, details in QT

AA

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28,374 views • 4 months ago •via X (Twitter)

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i trained this computer vision model just by asking for "tennis players", using roboflow rapid and SAM3 the model picks out Alcaraz and Sinner, while correctly avoiding the ball boys and fans then I exported the model to python and wrote a script to filter the data, track movement history, and annotate the final video this was a quick experiment but I could expand the project to track player speeds, distance covered, shot count, etc. could use this for a sports analytics app that tracks live matches and calculates custom metrics

i trained this computer vision model just by asking for "tennis players", using roboflow rapid and SAM3 the model picks out Alcaraz and Sinner, while correctly avoiding the ball boys and fans then I exported the model to python and wrote a script to filter the data, track movement history, and annotate the final video this was a quick experiment but I could expand the project to track player speeds, distance covered, shot count, etc. could use this for a sports analytics app that tracks live matches and calculates custom metrics

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I can't stop playing with Rapid Roboflow. The process is so smooth that you can just: - Go to the rapid roboflow platform - Drop a few images or a short video - Type a prompt like “yellow cap” or “person wearing red hat” - Instantly get a working detection model No training, no GPUs, no cloud setup. Plug it straight into your app. It’s also baked into Roboflow Workflows, so you can instantly build automations like “count vials crossing this line” in minutes. Computer vision just went from weeks in seconds.

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typing "player with a red shirt" was all it took to train this computer vision model just a few minutes to train the model, export to python, and create this video i'll keep testing roboflow rapid and will report back with the results in the meantime, enjoy the magic of Zlatan

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