YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World

YOLOv1 revolutionized object detection by treating it as a single regression problem, enabling real-time performance unlike slower, multi-stage methods. The model works by dividing an image into a grid, and the cell containing an object's center becomes responsible for its detection. Each grid cell then predicts multiple bounding boxes, confidence scores for those boxes, and class probabilities for the object. This entire process is handled by a single, unified convolutional neural network that looks at the image only once to make all predictions simultaneously.