YOLO Skill
YOLO, or You Only Look Once, is a real-time object detection system known for its speed and accuracy. It was introduced by Joseph Redmon and Santosh Divvala in 2016, and the architecture has undergone several iterations since then. YOLO's distinctive feature is its ability to detect and classify objects in images or video frames in a single forward pass through the neural network.The YOLO architecture divides the input image into a grid and predicts bounding boxes and class probabilities directly from this grid. This eliminates the need for region proposal networks and allows YOLO to make predictions with impressive speed. YOLO can detect multiple objects within an image simultaneously, making it particularly useful for applications where real-time processing is crucial, such as in autonomous vehicles, surveillance systems, and robotics.Several versions of YOLO have been released, including YOLOv2, YOLOv3, and YOLOv4, each introducing improvements in terms of accuracy and speed. YOLO has become a widely adopted object detection framework and has influenced the development of other real-time detection systems in the field of computer vision.
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