关键词: 深度学习, 剪枝, 卷积神经网络

Abstract:

Aiming at the characteristics of convolutional neural networks such as YOLOv3 using more convolutional layer structure and uniform size of convolution kernel, a parameter compression method for convolutional layer pruning calculation is proposed. Based on the convolutional layer weight parameter, a formula for measuring the importance of the convolutional layer is designed. The importance of the convolutional layer relative to the whole network is evaluated. The importance of the convolutional layer is calculated and the scores are sorted. The sparse value assignment strategy is formulated. The retraining operation is performed to ensure that the performance of the model is not degraded, and the sparse value of each convolution layer and the convolution filter are obtained, and the structured pruning calculation of the model is completed. The parameter compression method of structured pruning of the YOLOv3 convolutional layer is implemented on Darknet, which not only compresses the YOLOv3 parameter by 1.5 times, but also reduces the calculation by 1.6 times.

Key words: deep learning, pruning, convolutional neural network