Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Build a Live Inference Application with Ultralytics YOLO | Streamlit, CLI & Python 🚀 🤯

Running models in real time is a common requirement for modern computer vision applications. In this tutorial, we walk you through the process of building and running a live inference application using Ultralytics YOLO11 and Streamlit. We’ll start with a walkthrough of the live inference documentation, then show how to launch the application using both the CLI and Python modes. You’ll learn how to configure different settings, switch between models, and test the app with object detection and instance segmentation models in real time. Chapters: 00:00 - Introduction to the live inference application 00:28 - Live inference application documentation walkthrough 01:59 - Running the application with CLI and Python 02:43 - Configuring application settings 03:23 - Testing application with YOLO11 object detection model 05:02 - Testing application with YOLO11 instance segmentation model 🔗 Explore more ➡️ https://docs.ultralytics.com/guides/streamlit-live-inference/ Ultralytics Resources: 🏢 About Us: https://ultralytics.com/about 💼 Join Our Team: https://ultralytics.com/work 📞 Contact Us: https://ultralytics.com/contact 💬 Discord Community: https://discord.com/invite/ultralytics 📄 Ultralytics License: https://ultralytics.com/license #ultralytics #yolo11 #liveinference #streamlit #computervision #objectdetection #segmentation #python