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Animals segmentation & tracking using Ultralytics YOLO26 🫎 How I created this demo: - Data annotation using Ultralytics Platform. - YOLO26 model training using Platform. - Downloaded the trained model. - Run Inference on video files locally. #animal #ai #MachineLearning

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Muhammad Rizwan Munawar

2,011 subscribers

21,656 görüntüleme • 1 ay önce •via X (Twitter)

Bilim & Teknoloji#animal#ai#MachineLearning
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Run promptable segmentation with Ultralytics YOLO26-E! 🧠 Segment objects using natural language prompts with YOLO26-E, ideal for flexible visual search, rapid annotation, and interactive computer vision workflows. Get started ➡️ #Ultralytics #YOLO26 #AI #Research
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Run promptable segmentation with Ultralytics YOLO26-E! 🧠 Segment objects using natural language prompts with YOLO26-E, ideal for flexible visual search, rapid annotation, and interactive computer vision workflows. Get started ➡️ #Ultralytics #YOLO26 #AI #Research

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Football players detection + tracking using Ultralytics YOLO26 ⚽️ It took me 30 minutes to train the YOLO26 model on a custom dataset using Ultralytics Platform. Later, I used the object tracker (Bytetrack) for tracking the players; tracking could be improved further, and I am working on it this week. More info 👇 #soccer #football #MachineLearning

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Vehicle tracking using Ultralytics YOLO26 👀 In this demo, I trained the YOLO26 model on a custom dataset and later used an object tracker (Bytetrack) to track the objects in the video file. Note: I also performed testing with Botsort, which is also a good tracker, but it's slow in comparision to Bytetrack. #Tracking #ComputerVision #MachineLearning

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People counting in zones using Ultralytics YOLO26 🧑‍🤝‍🧑 In this demo, people are moving on elevators. I used a custom-trained YOLO26 model to detect and track each person, then applied the object counting in zones solution to count the number of people within each zone. 🙂 #people #retail #MachineLearning

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Pothole detection on the road in real time using Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AI

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⭐️3D Model Live Tracking Showcase ⭐️ Live Demo of the 3D model i created using Blender!!

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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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The consistency of Kling 3.0 Motion Control is just insane and it’s all on mobile now. 💃🤯 I created this by mapping a dance onto a static image using the Pollo AI app. The physics and facial details stay 100% on the model. 💎 Right now, Kling 3.0 is 50% OFF! PolloAI Creators #Kling3 #PolloAI #AI #ParadoxPixels

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The Kling Motion Control 3.0 Challenge is live, and I just joined! I created this AI-powered motion video using Kling AI, and it’s been super fun to bring my ideas to life. Watch how Sadie Sink moves #KlingMotionControl3

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