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

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AA

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28,430 görüntüleme • 9 ay önce •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
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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

AA

293,837 görüntüleme • 10 ay önce

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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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.

Marco Franzon

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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
0:16

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

AA

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Making Coffee ad using GPT Image 2 x Seedance 2.0 on Higgsfield Same workflow as before ↓
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when machine learning turn into deep learning.
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Tried doing basic machine learning in Blender 3.5 #b3d #geometrynodes

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Opus 5 created this Architectural model after looking at 34 images in about 1 hour using the official Blender MCP. More details below.
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Nemotron 3 Ultra versus DeepSeek V4 versus MiniMax M3 versus Qwen 3.7 Max. Same two prompts. Four frontier models. One honest comparison. Fast. Genuinely good. More impressive than the benchmarks suggested. Full results below. Bookmark this before your next model decision.
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Nemotron 3 Ultra versus DeepSeek V4 versus MiniMax M3 versus Qwen 3.7 Max. Same two prompts. Four frontier models. One honest comparison. Fast. Genuinely good. More impressive than the benchmarks suggested. Full results below. Bookmark this before your next model decision.

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In a new PS5 Pro Technical Seminar, PS5 Pro Lead Architect Mark Cerny shares in-depth details on the console and reveals a deeper collaboration with AMD to drive innovation in graphics and gameplay through machine learning:
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me watching a fucking anime about volleyball
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Sensitive content

me watching a fucking anime about volleyball

j a d e ! ! ☆🕯️

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😍⚠️And it’s out in J Cell Science!! Our review on the opportunities and challenges of using Machine Learning in Microscopy research 🤖🔬! From using ML to curate data to using ML to give us more KNOWLEDGE about our data (i.e. Interpretation 😉) DOI: (1/n) ⬇️
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