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Yolo is an abbreviation of 'you only live once,' an expression used as justification for or in support of doing something carefree one normally wouldn’t do (because of expense, danger, risk of seeming foolish, etc.) Object detection is a computer vision task that uses neural networks to localize and classify objects in images First introduced by joseph redmon et al

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In 2015, [1] yolo has undergone several iterations and improvements, becoming one of the most popular object detection frameworks. Yolo is very fast at the test time because it uses only a single cnn architecture to predict results and class is defined in such a way that it treats classification as a regression problem. Yolo (you only look once), a popular object detection and image segmentation model, was developed by joseph redmon and ali farhadi at the university of washington

Launched in 2015, yolo gained popularity for its high speed and accuracy.

Ultralytics supports a wide range of yolo models, from early versions like yolov3 to the latest yolo11 The tables below showcase yolo11 models pretrained on the coco dataset for detection, segmentation, and pose estimation.