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CenterNet is a key framework in anchor-free object detection, with many recent detectors building upon its concepts. This tutorial covers the basics of CenterNet, explaining its workings, loss functions, and backbone comparisons. Objects are represented as keypoints at the center of bounding boxes, with the model predicting dimensions based on input image size and output stride. The model includes three heads for key points, local offset, and object size, with heat map values decreasing exponentially from the object center. Each class has its own heat map channel, offering a comprehensive understanding of object detection techniques.
