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CenterNet is a crucial development in anchor-free object detection, with recent detectors being based on it. This tutorial explains how CenterNet works by representing objects as points, using three heads for key point heat maps, local offset, and object size. The model predicts object locations based on input image dimensions and model output stride. Heat map values are assigned exponentially based on distance from the object center, with C channels for different object classes. This video provides insights into CenterNet's fundamentals and compares its various backbones.