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Muhammad Rizwan Munawar

@muhammdrizwanmr • 1,734 subscribers

Computer vision | Content creator @ultralytics | Open source contributor | Coming soon YOLO26 | Vision language models

Shorts

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

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

42,160 views

Detect small objects using @Ultralytics YOLO26 + SAHI 😍 A perfect combo when speed isn’t the priority, but accuracy is everything, especially when detecting tiny or distant objects in your images. learn how it works 👇 #AI #objectdetection #trafficanalysis

Detect small objects using @Ultralytics YOLO26 + SAHI 😍 A perfect combo when speed isn’t the priority, but accuracy is everything, especially when detecting tiny or distant objects in your images. learn how it works 👇 #AI #objectdetection #trafficanalysis

39,043 views

Mediapipe is cool, but Ultralytics YOLO26 Pose is on another level 🔥🔥🔥 I ran a quick comparison between mediapipe and YOLO pose, and while both have their strengths, YOLO26 blew me away. My findings👇 Raw video credit: NVIDIA Team 😀 #AI #PoseEstimation #People

Mediapipe is cool, but Ultralytics YOLO26 Pose is on another level 🔥🔥🔥 I ran a quick comparison between mediapipe and YOLO pose, and while both have their strengths, YOLO26 blew me away. My findings👇 Raw video credit: NVIDIA Team 😀 #AI #PoseEstimation #People

83,957 views

Real-time bakery item counting using Ultralytics YOLO26 😍 In food production lines, counting sounds simple until it isn’t. Items move fast, overlap on conveyors, change orientation, and sometimes partially occlude each other. Whether it’s ice-cream cones on a belt or cream being filled into nests, maintaining accurate counts in real time is critical for quality control and throughput. More info 👇 #Bakery #Retail #MachineLearning

Real-time bakery item counting using Ultralytics YOLO26 😍 In food production lines, counting sounds simple until it isn’t. Items move fast, overlap on conveyors, change orientation, and sometimes partially occlude each other. Whether it’s ice-cream cones on a belt or cream being filled into nests, maintaining accurate counts in real time is critical for quality control and throughput. More info 👇 #Bakery #Retail #MachineLearning

24,908 views

Smart parking pipeline in real-time with Ultralytics YOLO26 🚗 🅿️ Over the weekend, I built a parking management system that monitors slot occupancy (occupied vs available). In busy parking areas where vehicles partially overlap slots or enter at angles, the system still maintains accurate detection and status updates in real time. More information 👇 #yolo26 #parkingproblem #machinelearning

Smart parking pipeline in real-time with Ultralytics YOLO26 🚗 🅿️ Over the weekend, I built a parking management system that monitors slot occupancy (occupied vs available). In busy parking areas where vehicles partially overlap slots or enter at angles, the system still maintains accurate detection and status updates in real time. More information 👇 #yolo26 #parkingproblem #machinelearning

37,395 views

Car-to-curb distance estimation using Ultralytics YOLO26. 🚗 In parking lots, knowing the exact distance between a vehicle and the curb can help prevent minor collisions and improve parking precision. In this demo, a car is detected and tracked, and the system calculates the distance from the curb in real time in pixel space, showcasing how computer vision can turn simple camera feeds into spatial awareness tools. Implementation details 👇 #cardistance #visionai #ROADSAFETYCONCERN

Car-to-curb distance estimation using Ultralytics YOLO26. 🚗 In parking lots, knowing the exact distance between a vehicle and the curb can help prevent minor collisions and improve parking precision. In this demo, a car is detected and tracked, and the system calculates the distance from the curb in real time in pixel space, showcasing how computer vision can turn simple camera feeds into spatial awareness tools. Implementation details 👇 #cardistance #visionai #ROADSAFETYCONCERN

27,503 views

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

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

30,677 views

Power of CoreML + Ultralytics YOLO11 on Apple devices! 85 FPS 🥳 This comparison highlights how exporting your PyTorch model to CoreML can significantly boost real-time inference by leveraging Apple’s optimized hardware stack. More details👇 #MachineLearning #research

Power of CoreML + Ultralytics YOLO11 on Apple devices! 85 FPS 🥳 This comparison highlights how exporting your PyTorch model to CoreML can significantly boost real-time inference by leveraging Apple’s optimized hardware stack. More details👇 #MachineLearning #research

85,285 views

Car parts detection running on the SiMa.ai DevKit 3.0 😍 I recently had the opportunity to test YOLO11 for car parts detection on the devkit to see how well the model performs on edge hardware. The system processes frames in real time while maintaining efficient resource usage, making it a great fit for applications where low latency and reliability are critical. Additional details👇 #carparts #manufacturing #edgeai

Car parts detection running on the SiMa.ai DevKit 3.0 😍 I recently had the opportunity to test YOLO11 for car parts detection on the devkit to see how well the model performs on edge hardware. The system processes frames in real time while maintaining efficient resource usage, making it a great fit for applications where low latency and reliability are critical. Additional details👇 #carparts #manufacturing #edgeai

14,040 views

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Packet counting using Ultralytics YOLO26 🚀 Here, I trained the YOLO26 model on a custom dataset and later used an object tracker (Bytetrack) + Ultralytics Solutions to count the objects in the video file. #packets #counting #MachineLearning

Packet counting using Ultralytics YOLO26 🚀 Here, I trained the YOLO26 model on a custom dataset and later used an object tracker (Bytetrack) + Ultralytics Solutions to count the objects in the video file. #packets #counting #MachineLearning

Muhammad Rizwan Munawar

120,541 views • 7 days ago

Real-time traffic analysis using Ultralytics YOLO26 🚗 Traffic analysis sounds simple: detect cars, count them, done. But in reality, it’s messy. Cars overlap at signals. Tracking breaks for a few frames. Multiple camera streams don’t scale easily. And suddenly, your “traffic insights” aren’t that reliable. 👉 Why this matters: In production, models usually work. What breaks is everything around them, handling streams, maintaining tracking, and turning detections into something useful. More information 👇 #computervision #traffic #ai

Real-time traffic analysis using Ultralytics YOLO26 🚗 Traffic analysis sounds simple: detect cars, count them, done. But in reality, it’s messy. Cars overlap at signals. Tracking breaks for a few frames. Multiple camera streams don’t scale easily. And suddenly, your “traffic insights” aren’t that reliable. 👉 Why this matters: In production, models usually work. What breaks is everything around them, handling streams, maintaining tracking, and turning detections into something useful. More information 👇 #computervision #traffic #ai

Muhammad Rizwan Munawar

11,375 views • 1 month ago

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