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CenterNet is a key innovation in anchor-free object detection, with many cutting-edge detectors built upon its principles. This tutorial explores the fundamentals of CenterNet, explaining how it represents objects as key points and the structure of its prediction outputs. The model utilizes three key heads - key point heatmap, local offset, and object size - with heat map values assigned based on exponential distance from the object center. Understanding CenterNet's workings and loss functions is crucial for leveraging its advantages in object detection tasks.