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CenterNet is a significant advancement in anchor-free object detection, with recent state-of-the-art detectors being based on it. This video tutorial on LearnopenCV explains how CenterNet works by representing objects as key points, with a model input image of width W and height H. The output prediction is of width floor W by R and height floor H by R, with R being the model output stride. CenterNet has three heads for key point heat map, local offset, and object size, with heat map values assigned using exponential distance. The heat map values decrease exponentially as they move away from the object center.
