You Only Look Once

You Only Look Once
Original author(s)Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
Initial release2015
Written inPython
Type
Websitepjreddie.com/darknet/yolo/
Objects detected with OpenCV's Deep Neural Network module by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes

You Only Look Once (YOLO) is a series of real-time object detection systems based on convolutional neural networks. First introduced by Joseph Redmon et al. in 2015,[1] YOLO has undergone several iterations and improvements, becoming one of the most popular object detection frameworks.[2]

The name "You Only Look Once" refers to the fact that the algorithm requires only one forward propagation pass through the neural network to make predictions, unlike previous region proposal-based techniques like R-CNN that require thousands for a single image.

  1. ^ Redmon, Joseph; Divvala, Santosh; Girshick, Ross; Farhadi, Ali (2016-05-09). "You Only Look Once: Unified, Real-Time Object Detection". arXiv:1506.02640 [cs.CV].
  2. ^ Terven, Juan; Córdova-Esparza, Diana-Margarita; Romero-González, Julio-Alejandro (2023-11-20). "A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS". Machine Learning and Knowledge Extraction. 5 (4): 1680–1716. arXiv:2304.00501. doi:10.3390/make5040083. ISSN 2504-4990.