YOLOv5: Real-Time Object Detection Algorithm | Ultralytics

YOLOv5 is a real-time object detection algorithm developed by Ultralytics. It builds on the success of previous YOLO versions and achieves state-of-the-art performance on the COCO dataset, with improved speed and accuracy. YOLOv5 is a single-stage detector that uses a deep neural network to predict bounding boxes and class probabilities for objects in an image. It's designed to be easy to use, fast, and accurate, making it an ideal choice for real-time applications such as autonomous driving, robotics, and surveillance systems. YOLOv5 is open-source and can be used for both commercial and non-commercial purposes.