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

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28,430 views • 6 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
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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 views • 7 months ago

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

176,045 views • 7 months ago

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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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

239,248 views • 7 months ago

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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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:

PlayStation

718,314 views • 1 year ago

😍⚠️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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😍⚠️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) ⬇️

Inês Cunha

21,152 views • 1 year ago

Your creative workflow will never be the same! Introducing Nano Banana 🍌 Google’s most advanced image-generation model yet! One-shot editing. Photorealistic results. Flawless character consistency. Try it today in ImagineArt Studio.
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Your creative workflow will never be the same! Introducing Nano Banana 🍌 Google’s most advanced image-generation model yet! One-shot editing. Photorealistic results. Flawless character consistency. Try it today in ImagineArt Studio.

ImagineArt

381,116 views • 11 months ago

You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.
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You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.

Workshop AI

904,641 views • 3 months ago

Congratulations to iAgent, the February Altura Grant Fund winner! 🎉 With iAgent Protocol's Visual Learning Model, players transform into innovators, using decentralized computing to train AI agents directly through in-game video content. 🔗
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Congratulations to iAgent, the February Altura Grant Fund winner! 🎉 With iAgent Protocol's Visual Learning Model, players transform into innovators, using decentralized computing to train AI agents directly through in-game video content. 🔗

Altura

17,269 views • 1 year ago

AI video consistency starts before motion. For this 15s cooking short, we first generated a clean character storyboard in PixVerse. Then used it as reference for the video. Same character. Clear story beats. Camera direction. Motion details. RT + Follow + Reply = Storyboard Workflow
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AI video consistency starts before motion. For this 15s cooking short, we first generated a clean character storyboard in PixVerse. Then used it as reference for the video. Same character. Clear story beats. Camera direction. Motion details. RT + Follow + Reply = Storyboard Workflow

PixVerse

22,281 views • 2 months ago

Great prompt, but fine details won't survive the grid. Nano Banana Pro generates each in ~1k resolution. Product text gets lost. This workflow fixes that: → Automatically extracts all 9 grid image → You select your favorite → Upscales to 4K using your input as reference
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Great prompt, but fine details won't survive the grid. Nano Banana Pro generates each in ~1k resolution. Product text gets lost. This workflow fixes that: → Automatically extracts all 9 grid image → You select your favorite → Upscales to 4K using your input as reference

rob - comfyui

84,058 views • 6 months ago

i made four sport app prototypes this week, using roboflow rapid + SAM3 + python these ideas could be taken further to make $$$ example: SportRadar is a company that sells analytics to sports leagues and betting firms, with a market cap of $6 billion (!!) a few ideas - for coaches: automated analysis of trainings, tracking stats by player, scouting to prepare for future opponents - for players: compare your form against pros to improve your technique - for amateur leagues: turn hour-long full match footage into automated video reel highlights - for broadcasters: better visuals and stats during live events - for betting / prediction markets: real-time analysis of player performance to predict results
0:14

i made four sport app prototypes this week, using roboflow rapid + SAM3 + python these ideas could be taken further to make $$$ example: SportRadar is a company that sells analytics to sports leagues and betting firms, with a market cap of $6 billion (!!) a few ideas - for coaches: automated analysis of trainings, tracking stats by player, scouting to prepare for future opponents - for players: compare your form against pros to improve your technique - for amateur leagues: turn hour-long full match footage into automated video reel highlights - for broadcasters: better visuals and stats during live events - for betting / prediction markets: real-time analysis of player performance to predict results

AA

219,895 views • 7 months ago

Video Model Comparison: Image2Video Same input image + text prompt on each model: • Pika 2.0 • OpenAI Sora • Runway Gen-3 • Kling AI 1.6 • Luma Dream Machine • Hailuo MiniMax I used the same prompt that I used to generate this image in Midjourney and chose the best results for each model tested. The prompt didn't include specific camera motion, so it was interesting to see each model's interpretation:
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Video Model Comparison: Image2Video Same input image + text prompt on each model: • Pika 2.0 • OpenAI Sora • Runway Gen-3 • Kling AI 1.6 • Luma Dream Machine • Hailuo MiniMax I used the same prompt that I used to generate this image in Midjourney and chose the best results for each model tested. The prompt didn't include specific camera motion, so it was interesting to see each model's interpretation:

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How to create this kind of video game interface animation with GPT Image 2 and Seedance 2 why is it possible now thanks to GPT Image 2? This model is really good in terms of details and design interface. Also, this model allows you to rework multiple times an image without damaging the quality let me share my workflow👇
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How to create this kind of video game interface animation with GPT Image 2 and Seedance 2 why is it possible now thanks to GPT Image 2? This model is really good in terms of details and design interface. Also, this model allows you to rework multiple times an image without damaging the quality let me share my workflow👇

INK

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We’re amazed: AMD now beats Nvidia in inference for Image-to-Video generation. 20% faster and 35% cheaper when using our Higgsfield DoP model. AMD MI300X also has a very easy setup. Details and tests are below. 🧩 1/8
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We’re amazed: AMD now beats Nvidia in inference for Image-to-Video generation. 20% faster and 35% cheaper when using our Higgsfield DoP model. AMD MI300X also has a very easy setup. Details and tests are below. 🧩 1/8

Higgsfield AI 🧩

841,949 views • 1 year ago

The first natively trained 1-bit model: BitNet 2B. Trained on 4 trillion tokens. that can run on CPUs like Apple M2 Native 1.58-bit weights and 8bit activations W158A8 Outperforms LLaMA &close to Qwen 2.5 1.5B in while using only 0.4GB memory versus 2GB and processes tokens 40%
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The first natively trained 1-bit model: BitNet 2B. Trained on 4 trillion tokens. that can run on CPUs like Apple M2 Native 1.58-bit weights and 8bit activations W158A8 Outperforms LLaMA &close to Qwen 2.5 1.5B in while using only 0.4GB memory versus 2GB and processes tokens 40%

Md Ismail Šojal 🕷️

43,647 views • 5 months ago

Comparing AI Video Models: Creepy found footage Pika 2.0 Kling AI 1.5 OpenAI Sora Google Veo-2 Runway Gen-3 Hunyuan Video Hailuo MiniMax Luma Dream Machine I ran the same prompt in each model 3-4x and chose the best results:
0:10

Comparing AI Video Models: Creepy found footage Pika 2.0 Kling AI 1.5 OpenAI Sora Google Veo-2 Runway Gen-3 Hunyuan Video Hailuo MiniMax Luma Dream Machine I ran the same prompt in each model 3-4x and chose the best results:

Heather Cooper

71,788 views • 1 year ago

Haven't been to a conference in a while, really excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)
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Haven't been to a conference in a while, really excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)

Abhishek Gupta

10,803 views • 1 year ago

An amazing technology! Look at the video of the autogenerated machine-learning 3D map using CARTOSOUND FAM Module with the ICE catheter. @BiosenseWebster Jonathan Yarnitsky Grace Ha Montefiore Health System Montefiore Heart White Plains Hospital Heart Rhythm Society American College of Cardiology Xiaodong Zhang (Andy), MD, PhD, FHRS, FACC Europace Editor-in-Chief
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An amazing technology! Look at the video of the autogenerated machine-learning 3D map using CARTOSOUND FAM Module with the ICE catheter. @BiosenseWebster Jonathan Yarnitsky Grace Ha Montefiore Health System Montefiore Heart White Plains Hospital Heart Rhythm Society American College of Cardiology Xiaodong Zhang (Andy), MD, PhD, FHRS, FACC Europace Editor-in-Chief

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