Evolution of YOLO Models

YOLO is a popular series of deep-learning models designed for real-time object detection. The evolutionary journey of YOLO models can be traced through several major versions, each building upon the successes and shortcomings of its predecessor.

YOLOv1

The original YOLO model was a convolutional neural network (CNN) trained on the PASCAL VOC object detection dataset. YOLO adopted a unique approach by dividing the input image into a grid, with each grid cell responsible for predicting bounding boxes and associated class probabilities. One of the significant advantages of this model was its ability to use a singular CNN architecture to process the input image and make predictions in a single forward pass, making it notably fast. However, despite its innovative design, the YOLO model had its limitations. Relative to other models of its era, YOLO had a lower accuracy. Specifically, it faced challenges in detecting small objects and objects that were closely spaced. ...