Using YOLO for Real-Time Object Detection with Koyeb GPUs

This comprehensive guide explains how to implement real-time object detection using the YOLO (You Only Look Once) algorithm. YOLO stands out by processing images in a single pass to detect objects, making it highly efficient for real-time applications in surveillance, robotics, and autonomous driving. The guide covers the theory behind YOLO, its working mechanism, and step-by-step instructions for implementation. YOLO divides an image into a grid, evaluates each cell for objects, generates bounding boxes with confidence scores, and identifies object classes within those boxes. The guide provides instructions to set up a project environment, including creating a virtual environment and installing necessary libraries like PyTorch, Ultralytics YOLO, OpenCV, and Streamlit. It also includes code snippets for building a Streamlit application that uses a YOLOv8 model for object detection and tracking in real-time video streams. Additionally, the guide covers advanced YOLO applications such as object counting, cropping, and blurring, providing corresponding code examples for each task. Practical real-world applications of YOLO, such as crowd management, inventory management, and wildlife monitoring, are highlighted. Users are guided on deploying the YOLO application using Koyeb's GPUs for enhanced performance. The tutorial emphasizes the ease of use and versatility of YOLO, showcasing its capabilities in various computer vision tasks.