Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Build a Client Server Object Detection System with Python using Ultralytics YOLO | P2P Net 🌐

Build a real-time peer-to-peer object detection system with a server-client architecture using Python. In this tutorial, you'll learn how to set up a communication flow between clients and a central server, where object detection runs on the client side and results are sent to the server for storage and visualization. We’ll walk through both the server-side and client-side code, establish peer-to-peer connections, run detection in real time, and save the outputs to a CSV file. You'll also understand the importance of designing strong business logic when handling large-scale detection pipelines. Chapters: 00:00 - Introduction to peer-to-peer communication 01:02 - How the peer-to-peer connection works 01:10 - Server-side setup and code walkthrough 04:17 - Saving detection results from the client to a CSV file 04:40 - Client-side setup and code walkthrough 05:57 - Client-side detection and result transmission 06:59 - Executing both server and client code 08:39 - Importance of business logic at scale 08:57 - Conclusion and summary 🔗 Client code ➡️ https://drive.google.com/file/d/13qZVoWhRGJ_Mu982i2xwLn2jfF4ZnXfi/view?usp=drive_link 🔗 Server code ➡️ https://drive.google.com/file/d/1h8Ba231e2LhtDsu6wbaUJYtqaKyyqJkz/view?usp=drive_link Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #objectdetection #pythonprojects #clientserverarchitecture #edgeai #computervision #ultralytics