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In this lecture, I take you through the fascinating journey of how YOLO (You Only Look Once) revolutionized computer vision. Unlike older object detection methods that relied on region proposals and multiple passes over an image, YOLO introduced the idea of detecting objects in a single forward pass, making real-time detection practical and accessible. We begin by discussing the origins of object detection and the limitations of pre-YOLO models like R-CNN and Fast R-CNN. From there, I explain the YOLO architecture in detail, covering how the image is divided into grid cells, how bounding boxes and class probabilities are predicted, and why this approach is both elegant and powerful. You will also see how YOLO evolved across different versions, from the original YOLOv1 to the highly optimized YOLOv8 used today. Along the way, I share examples, code demonstrations, and real-world applications where YOLO plays a crucial role - from autonomous driving to video surveillance and sports analytics. This lecture is part of Vizuara’s Computer Vision Bootcamp, where we explore the fundamentals and cutting-edge advances in computer vision step by step. If you are curious about how machines are able to look at images and instantly recognize objects, this lecture will give you both the intuition and the technical depth you need. https://computervision.vizuara.ai/