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Muhammad Rizwan Munawar

@muhammdrizwanmr • 2,376 subscribers

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

Shorts

Railway operators manage thousands of kilometers of track and critical infrastructure every day!🚆 With Ultralytics YOLO26, teams can automatically monitor assets such as insulators, rail clamps, semaphores, and towers in images and video, improving asset visibility and supporting predictive maintenance workflows. Explore more ➡️ #RailwayOperations #MachineLearning #AI

Railway operators manage thousands of kilometers of track and critical infrastructure every day!🚆 With Ultralytics YOLO26, teams can automatically monitor assets such as insulators, rail clamps, semaphores, and towers in images and video, improving asset visibility and supporting predictive maintenance workflows. Explore more ➡️ #RailwayOperations #MachineLearning #AI

13,529 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

84,350 views

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

22,412 views

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

42,672 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

85,378 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

39,529 views

Object tracking with FastSAM using Ultralytics Python package 🎉💪 FASTSAM-s model is employed for real-time object detection, combined with botsort object tracking to further boost performance. 🐻 Code link 👇 #FastSAM #MachineLearning #Research

Object tracking with FastSAM using Ultralytics Python package 🎉💪 FASTSAM-s model is employed for real-time object detection, combined with botsort object tracking to further boost performance. 🐻 Code link 👇 #FastSAM #MachineLearning #Research

12,563 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

37,776 views

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 views

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 views

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 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

25,253 views

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 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

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 views

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 views

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 views

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 views

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 views

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Packet counting as they move through the production line using Ultralytics YOLO26 😍 #MachineLearning #Packets #Logistics
0:32
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Packet counting as they move through the production line using Ultralytics YOLO26 😍 #MachineLearning #Packets #Logistics

Muhammad Rizwan Munawar

87,311 views • 7 days ago

What if cows could have their own pose estimation model? 🐄 Cow pose estimation using Ultralytics YOLO26 Pose, with 20 keypoints trained on a custom dataset. The dataset is also available for testing directly in the Platform. Link 👇 Computer vision keeps getting more interesting. 👀 #Cow #Pose #MachineLearning
0:23
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What if cows could have their own pose estimation model? 🐄 Cow pose estimation using Ultralytics YOLO26 Pose, with 20 keypoints trained on a custom dataset. The dataset is also available for testing directly in the Platform. Link 👇 Computer vision keeps getting more interesting. 👀 #Cow #Pose #MachineLearning

Muhammad Rizwan Munawar

21,264 views • 1 month ago

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
0:25
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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

Muhammad Rizwan Munawar

46,738 views • 4 months ago

Workouts monitoring using Ultralytics YOLO26 pose 🏃‍♂️‍➡️ Imagine tracking your workouts using YOLO26, whether it's leg press, bench press, or leg extension, with just 2 lines of code! I've shared the command for running it, making it easy to track your workouts even on a CPU-based system. You don’t always need a high-end GPU; exporting the model to TorchScript or ONNX can boost performance on CPU devices. Code below 👇 #Workout #AI #MachineLearning
0:31
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Workouts monitoring using Ultralytics YOLO26 pose 🏃‍♂️‍➡️ Imagine tracking your workouts using YOLO26, whether it's leg press, bench press, or leg extension, with just 2 lines of code! I've shared the command for running it, making it easy to track your workouts even on a CPU-based system. You don’t always need a high-end GPU; exporting the model to TorchScript or ONNX can boost performance on CPU devices. Code below 👇 #Workout #AI #MachineLearning

Muhammad Rizwan Munawar

15,833 views • 1 month ago

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
0:22
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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 views • 4 months 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
0:31
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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 • 6 months ago

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
0:27
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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 views • 1 year ago

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