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CenterNet is a key technique in anchor-free object detection and has influenced recent state-of-the-art object detectors. This video tutorial explains how CenterNet works by representing objects as key points and utilizing a three-head model for predictions. The model outputs predictions based on input image dimensions and has heads for key point heat maps, local offset, and object size. Heat map values decrease exponentially from the object center, and there are C channels for different object classes. Understanding CenterNet fundamentals is crucial for implementing anchorless object detection effectively.
