Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Crop and Blur Objects with Ultralytics YOLO11 | Privacy Protection & Class-Based Filtering 🤯🚀

Object cropping and object blurring are two useful techniques in computer vision, commonly used for tasks like privacy protection, object-focused analysis, and dataset preparation. In this tutorial, we’ll walk through how to use Ultralytics YOLO11 to crop detected objects, blur specific classes, and build preprocessing pipelines for real-world applications. We’ll begin with the documentation walkthrough, followed by a quick demo showing how people blurring works in live video. After that, we’ll move into Python, where you’ll learn how to run the object cropping pipeline, filter specific classes, and visualize the cropped outputs. We’ll also explore the object blurring pipeline, understand the available arguments, and see how to blur only selected objects in a scene. Chapters: 00:00 - Introduction to object cropping and blurring solution 00:58 - Object cropping & blurring documentation overview 02:25 - People blurring demo 02:42 - Running object cropping in Python 03:15 - Cropping specific classes 04:30 - Visualizing cropped objects 04:57 - Running object blurring in Python 05:16 - Blurring solution arguments explained 06:52 - Blurring specific classes 07:16 - Conclusion and key takeaways 🔗 Object cropping code & docs ➡️ https://docs.ultralytics.com/guides/object-cropping 🔗 Object blurring code & docs ➡️ https://docs.ultralytics.com/guides/object-blurring Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #objectcropping #objectblurring #yolo11 #ultralytics #computervision #python