Muhammad Rizwan Munawar
Computer Vision Engineer at ultralytics
Islamabad, Islamabad Capital Territory, Pakistan
Summary
🤩
RockstarMuhammad Rizwan Munawar is a Computer Vision Engineer with six years’ experience building practical AI systems from research to production. Based in Islamabad, he focuses on object detection and tracking, contributing notable enhancements to YOLO-based projects—adding robust tracking labels, class-wise counting, heatmaps, and SAHI integration for improved video inference. An active open-source contributor and technical writer, he blends hands-on ML engineering with data-science sensibilities to deliver real-time analytics and deployable vision pipelines. He’s known for pragmatic improvements like efficient weight handling and blur options for privacy-preserving detections, reflecting an emphasis on usability and operational robustness.
6 years of coding experience
Stackoverflow
662reputation
92kreached
62answers
4questions
Github Skills (14)
object-detection10
computer-vision10
pytorch10
opencv10
machine-learning10
yolov710
python10
segmentation9
image-segmentation9
algorithms9
yolov56
deep-learning6
onnx6
training-data6
Programming languages (6)
Github contributions (5)
RizwanMunawar/yolov7-object-tracking
Aug 2022 - Dec 2022
YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 4 reviews, 47 commits in 3 months
Contributions summary:Muhammad primarily contributed to the object tracking functionality of the YOLOv7-based system. Their commits focused on enhancing the tracking process by adding labels to tracks, assigning unique colors, and implementing features to save track data and bounding box dimensions. Furthermore, they made improvements to weight file downloads and added an option for blurring detections. The user also made changes to the core `detect.py` and `detect_and_track.py` files.
pytorchdeep-learningyolov8object-detectioncomputer-vision
ultralytics/ultralytics
Jan 2023 - Apr 2025
Ultralytics YOLO11 🚀
Role in this project:ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:110 reviews, 375 PRs, 1881 pushes in 2 years 3 months
Contributions summary:Muhammad primarily contributed to the development of computer vision solutions within the Ultralytics YOLO repository, specifically related to object detection and tracking. They added features like SAHI integration for improved video inference, a multiple movable regions counting example, and addressed bugs in existing modules. Moreover, the user expanded the capabilities by incorporating class-wise object counting, heatmap functionality, and real-time analytics, demonstrating proficiency in enhancing the project's core functionalities.
pytorchdeep-learningyolov8object-detectiononnx
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