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CenterNet is a key anchor-free object detection model used as a basis for recent state-of-the-art anchorless object detectors. This tutorial explains the fundamentals of CenterNet, discussing how it represents objects as key points and utilizes three heads for predictions - key point heat map, local offset, and object size. The model operates by taking an input image and outputting predictions of a specific size based on the model output stride. Heat map values are assigned through an exponential distance method, with values decreasing as they move away from the object center.
