Ultralytics YOLO11 is a cutting-edge, state-of-the-art (SOTA) model that surpasses all previous YOLO versions in both speed and accuracy. YOLO11 can be used for a wide range of tasks, including Object Detection, Instance Segmentation, Pose Estimation, and Image Classification.

In this video tutorial, we’ll explore how to perform object detection, instance segmentation, pose estimation, and image classification using YOLO11on windows.
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**🧑🏻‍💻 My AI and Computer Vision Courses⭐*

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*Support Us on Patreon*
https://www.patreon.com/user?u=86750182
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*Don’t forget to connect with me*
👉 LinkedIn: https://www.linkedin.com/in/muhammad-moin-7776751a0/
🤖 GitHub: https://github.com/MuhammadMoinFaisal
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*⚒️Freelance Work*
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*For Consultation Call 📞*
https://www.upwork.com/freelancers/~010c0e127772f371efe

Happy Coding!

⌛Time Stamps⏳
Introduction & Demo: 00:00
Ultralytics Github Repo: 00:36
Setting up Virtual Environment: 03:21
Set up YOLO11: 04:58
Object Detection: 09:55
Object Segmentation: 13:40
Pose Estimation: 16:51
Image Classification: 19:51

Tags:
#yolo11 #yolo #ultralytics #objectdetection #objectsegmentation #instancesegmentation #poseestimation #imageclassification #yolov8 #opencvpython #opencv #computervision #computervisioncourse

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