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CenterNet is a key player in anchor-free object detection, with recent detectors building off its principles. This tutorial delves into how CenterNet functions, its Loss Functions, and a comparison of its backbones. Objects are represented as a Key Point (bounding box center), with the model predicting dimensions based on input image size and stride. The model includes three heads - key point heat map, local offset, and object size - with heat map values based on exponential distance from the object center. Each object class has its own channel in the heat map.
