<ul data-eligibleForWebStory="false"><li>YOLO (You Only Look Once) is a real-time object detection architecture known for its single regression problem approach, in contrast to two-stage detectors like Faster R-CNN.</li><li>It divides images into a grid for predicting bounding boxes and class probabilities, offering faster inference with a slight decrease in accuracy compared to traditional detectors.</li><li>YOLO has evolved through various versions, maintaining core components such as the backbone for feature extraction, the neck for feature aggregation, and the head for final predictions.</li><li>Despite its benefits in speed and real-time applications, YOLO has limitations that include a compromise on accuracy compared to two-stage detectors.</li></ul>

Exploring Foundation of YOLO:You Only Look Once

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