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ap generation, only the channel corresponding to the class of the object is used. Local offsets predict the offsets of the bounding box center from the corresponding keypoint. The object size outputs the width and height of the bounding box. CenterNet uses a focal loss function to train the model, which gives more weight to hard examples. It also uses L1 loss for regression tasks. The video explains different backbone architectures used in CenterNet, such as ResNet, DLA, and Hourglass. These backbones play a crucial role in the performance of the model. Understanding CenterNet's key components and loss functions is essential for anyone working on anchor-free object detection. CenterNet is a pivotal advancement in anchor-free object detection, serving as the foundation for recent anchorless object detectors. This video tutorial explores CenterNet's functionality, loss functions, and compares various backbone architectures. Objects are represented as key points, with the model predicting outputs based on input image dimensions. The model has three heads for key point heat maps, local offsets, and object sizes, utilizing exponential distance for heat map values. Focal and L1 loss functions are used for training, and different backbone architectures like ResNet and Hourglass impact model performance significantly. Understanding CenterNet's components and training methods is vital for anchor-free object detection projects.
