YOLO Series
YOLO, which stands for “You Only Look Once” is a popular real-time object detection algorithm in computer vision introduced in 2016. YOLO revolutionized object detection by proposing a different approach compared to traditional methods where it divided images into a grid and predicts the bounding boxes and class probabilities directly from the input image in a single pass.
YOLO operates at a high speed because it avoids the need for multiple passes through the image when compared to the two-stage complex region proposal object detection networks like Fast-RCNN and Faster-RCNN. The single forward pass of the YOLO network allows it to achieve real-time object detection on both images and videos.
Over the years, there have been several versions of YOLO, including the state of the arts (SOTA) YOLOv6, YOLOv7, YOLOv8, PP-YOLO, YOLOR, and the latest YOLO-NAS. Each version introduced improvements and architectural changes to enhance both speed and accuracy.
Read more about the comparison of different versions of the YOLO model through link1 and link2.
