Build a Flask Backend for Real-Time Weed Detection with YOLOv8 | Full-Stack AI Tutorial скачать в хорошем качестве

Build a Flask Backend for Real-Time Weed Detection with YOLOv8 | Full-Stack AI Tutorial

🔗 AI in Agriculture Course - https://www.augmentedstartups.com/ai-... App 6.3: Building the Backend Dive into the second part of our YOLO V8 Object Detection tutorial series! 🚀 In this video, we take the trained model and integrate it into a full-stack application. Learn how to build a backend using Flask and Flask APIs to process video data for real-time inference and tracking. Key Topics Covered: 🔧 Backend Setup: Learn how to integrate the YOLO V8 model for inference with Flask. 📹 Video Inference & Tracking: Set up inference on video data and implement object tracking using BotSort. 🖥️ Data Processing: Extract bounding boxes, IDs, and real-time tracking information. 🌐 Flask API Development: Create APIs to handle video uploads and stream processed frames to the frontend. 🛠️ Asynchronous Processing: Use threading to handle video processing efficiently. Chapters: 00:00 - Preparing the Trained Model for Backend Integration 00:29 - Building the YOLO V8 Inference Code 01:28 - Adding Object Tracking with BotSort 05:10 - Setting Up Flask API for Real-Time Streaming 07:33 - Handling Video Uploads and Processing Threads 08:30 - Generating Frames and Streaming JSON Data 09:58 - Connecting Backend to Frontend 💡 Pro Tips: Always test your backend with mock data before integrating with the frontend. Use threading to manage resource-heavy operations efficiently. 📌 Subscribe for more hands-on tutorials on AI, full-stack development, and machine learning in agriculture! 🔗 Follow My Links: Facebook:   / augmentedstartups   LinkedIn:   / riteshkanjee   X: https://x.com/augmentstartups Whatsapp AI Community: https://chat.whatsapp.com/JTuIB3eEfDR... #YOLOV8 #FlaskAPI #ObjectDetection #WeedDetection #FullStackDevelopment #AIinAgriculture