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CenterNet is a significant advancement in anchor-free object detection, with recent detectors being influenced by its concepts. This video tutorial from LearnopenCV delves into the fundamentals of CenterNet, explaining its working principles and loss functions. Objects are represented as key points or bounding box centers, with the model outputting predictions based on the input image dimensions and model output stride. The model features three heads for key point heat maps, local offsets, and object sizes, with heat map values decreasing exponentially from the object center. This understanding is crucial for grasping the intricacies of CenterNet.