YOLO (You Only Look Once) is a state-of-the-art, real-time object detection algorithm introduced in 2015 by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi¹. YOLO frames the object detection problem as a single regression problem, rather than a classification task, by spatially separating bounding boxes and associating probabilities to each detected object using a single convolutional neural network (CNN)².

Key Features of YOLO

Speed

YOLO is extremely fast because it processes images at 45 Frames Per Second (FPS) and can reach up to 91 FPS¹. This speed is achieved by using a single forward pass through the network to make predictions, unlike other models that require multiple passes².

High Detection Accuracy

YOLO achieves high detection accuracy with very few background errors. It generalizes well to new domains, making it suitable for applications requiring fast and robust object detection¹.

Open Source