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CenterNet is a milestone in anchor-free object detection. Some of the recent state-of-the-art anchorless object detectors are based on it. So its important to understand the fundamentals of CenterNet. Hey there! Welcome to LearnopenCV. In this video, well understand how CenterNet works, its Loss Functions and compare its various backbones. CenterNet represents an object as a point called a Key Point. This is the bounding box center. The model takes an input image of width W and height H and outputs a prediction of width floor W by R and height floor H by R. Here, R is the model output stride. It has three heads, the key point heat map, local offset, and object size. The heat map values are assigned using an exponential distance. It means at the object center, the heat map value is 1 and it decreases exponentially as it moves further away from the object center. There are C channels of the heat map where C is the number of object classes. During heat m
