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CenterNet is a key milestone in anchor-free object detection, with recent state-of-the-art detectors being based on it. This video tutorial explains how CenterNet works, focusing on its fundamentals, loss functions, and backbone comparisons. Objects are represented as key points, with the model predicting the bounding box size based on input image dimensions and model output stride. The model includes three heads for key point heat map, local offset, and object size, with heat map values assigned using exponential distance from the object center.