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CenterNet is a crucial advancement in anchor-free object detection, with recent state-of-the-art detectors built on its concepts. This tutorial delves into the fundamentals of CenterNet, explaining how it represents objects as key points and uses a model to predict their positions. The model includes three heads for heat maps, local offsets, and object sizes, with heat map values assigned based on exponential distance from the object center. Understanding CenterNet's workings, loss functions, and backbone options is key to mastering anchorless object detection techniques.
