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Muhammad Rizwan Munawar

@muhammdrizwanmr • 1,847 subscribers

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

Shorts

Depth anything v2 vs. Ultralytics YOLO26 depth | Speed comparison 🚀 For this demo, I compared yolo26m-depth with depth-anything-v2-small on an NVIDIA RTX 3050 using the same input settings. Even though I used the medium YOLO26 model, it delivered nearly 2× the throughput: 22 FPS vs. 11 FPS. #Vehicles #MachineLearning #Research

Depth anything v2 vs. Ultralytics YOLO26 depth | Speed comparison 🚀 For this demo, I compared yolo26m-depth with depth-anything-v2-small on an NVIDIA RTX 3050 using the same input settings. Even though I used the medium YOLO26 model, it delivered nearly 2× the throughput: 22 FPS vs. 11 FPS. #Vehicles #MachineLearning #Research

22,019 Aufrufe

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

84,350 Aufrufe

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

42,586 Aufrufe

Object detection + tracking + counting using Ultralytics YOLO26 🚀 👉 Object detection tells you what is in a scene. 👉 Tracking follows each object across video frames. 👉 Counting turns those movements into actionable data. YOLO26 delivers fast, accurate detections, while multi-object tracking maintains unique identities across frames. Add configurable counting lines or regions to measure entries, exits, directional movement, and other real-world traffic metrics. #vehicles #MachineLearning #Highway

Object detection + tracking + counting using Ultralytics YOLO26 🚀 👉 Object detection tells you what is in a scene. 👉 Tracking follows each object across video frames. 👉 Counting turns those movements into actionable data. YOLO26 delivers fast, accurate detections, while multi-object tracking maintains unique identities across frames. Add configurable counting lines or regions to measure entries, exits, directional movement, and other real-world traffic metrics. #vehicles #MachineLearning #Highway

16,069 Aufrufe

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

39,410 Aufrufe

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. #MachineLearning #AI #Research

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. #MachineLearning #AI #Research

23,386 Aufrufe

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

37,767 Aufrufe

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

85,378 Aufrufe

Floor plan segmentation using Ultralytics YOLO26! Architectural floor plans represent rich, unstructured spatial datasets. Applying instance segmentation enables precise identification of structural elements (walls, doors, corridors, and rooms), converting raw CAD images into structured digital assets. These outputs directly support spatial modeling, digital twin integration, and automated facility management. #Construction #AI #MachineLearning

Floor plan segmentation using Ultralytics YOLO26! Architectural floor plans represent rich, unstructured spatial datasets. Applying instance segmentation enables precise identification of structural elements (walls, doors, corridors, and rooms), converting raw CAD images into structured digital assets. These outputs directly support spatial modeling, digital twin integration, and automated facility management. #Construction #AI #MachineLearning

13,477 Aufrufe

New trackers released by Ultralytics 😍 botsort, bytetrack, ocsort, deepocsort, fasttrack, and tracktrack. Object tracking tells you which object is which across frames. I recently tested the newly released trackers in this demo. Here's what I found. 👇 #tracker #MachineLearning #Research

New trackers released by Ultralytics 😍 botsort, bytetrack, ocsort, deepocsort, fasttrack, and tracktrack. Object tracking tells you which object is which across frames. I recently tested the newly released trackers in this demo. Here's what I found. 👇 #tracker #MachineLearning #Research

18,403 Aufrufe

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

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

21,656 Aufrufe

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

25,151 Aufrufe

YOLOE-26 released by Ultralytics support text + visual prompts 🤯 This was a part of the YOLO26 release. It's a zero-shot, promotable model, almost like YOLO-World, but it also offers real-time object segmentation. Highlights👇 #AI #MachineLearning #YOLOE

YOLOE-26 released by Ultralytics support text + visual prompts 🤯 This was a part of the YOLO26 release. It's a zero-shot, promotable model, almost like YOLO-World, but it also offers real-time object segmentation. Highlights👇 #AI #MachineLearning #YOLOE

35,311 Aufrufe

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 Aufrufe

Instance vs semantic segmentation difference | Ultralytics🚀 🧩Instance segmentation answers: “Which pixels belong to each object?” 🎨Semantic segmentation answers: “Which pixels belong to each class?” #segmentation #MachineLearning #AI

Instance vs semantic segmentation difference | Ultralytics🚀 🧩Instance segmentation answers: “Which pixels belong to each object?” 🎨Semantic segmentation answers: “Which pixels belong to each class?” #segmentation #MachineLearning #AI

16,818 Aufrufe

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 Aufrufe

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

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

16,843 Aufrufe

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

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

14,980 Aufrufe

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 Aufrufe

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Vehicle 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. #vehicles #tracking #MachineLearning

Vehicle 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. #vehicles #tracking #MachineLearning

Muhammad Rizwan Munawar

46,738 Aufrufe • vor 3 Monaten

Pothole detection in real time using Ultralytics YOLO26 🕳️ Identify potholes accurately from images or video to support road maintenance, safety monitoring, and smart city infrastructure workflows. More info👇 #AI #SmartCities #Ultralytics

Pothole detection in real time using Ultralytics YOLO26 🕳️ Identify potholes accurately from images or video to support road maintenance, safety monitoring, and smart city infrastructure workflows. More info👇 #AI #SmartCities #Ultralytics

Muhammad Rizwan Munawar

18,819 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 4 Monaten

5️⃣ object detection and image segmentation use cases with Ultralytics 🎉 ✅ Tomato detection ✅ Teeth segmentation ✅ Pill detection ✅ Cabbage detection ✅ Fire detection 💡All models were trained on the custom dataset using Ultralytics YOLO11. #ml #research #usecases #ai

5️⃣ object detection and image segmentation use cases with Ultralytics 🎉 ✅ Tomato detection ✅ Teeth segmentation ✅ Pill detection ✅ Cabbage detection ✅ Fire detection 💡All models were trained on the custom dataset using Ultralytics YOLO11. #ml #research #usecases #ai

Muhammad Rizwan Munawar

12,501 Aufrufe • vor 1 Jahr

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