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CenterNet is a key milestone in anchor-free object detection, with recent state-of-the-art detectors based on its fundamentals. This tutorial delves into how CenterNet operates, its Loss Functions, and compares different backbones. Objects are represented as key points, with the model predicting bounding box dimensions based on input image size and output stride. The model has three heads for key point heat map, local offset, and object size, assigning values using an exponential distance from the object center.
