Research on vehicle detection based on improved YOLOX_S

Model pruning

The YOLOX network structure is highly complex, and the backbone and detection structures contain many convolution operations. Training the model once will produce many redundant weight parameters and channels, affecting the convergence speed. Effective model compression methods can be used to simplify the network structure and reduce the computational complexity of the model, and commonly used model compression techniques mainly include knowledge distillation, shared weights, model pruning, and low-rank decomposition. Knowledge distillation refers to constructing a…