GSDCPeleeNet：efficient lightweight convolutional neural based on PeleeNet
Ni Weijian, Qin Huibin · DOAJ (DOAJ: Directory of Open Access Journals) · 2021
Convolutional neural network plays an important role in various fields, especially in the field of computer vision, but its application in mobile devices is limited by the excessive number of parameters and computation. In view of the above problems, a new convolution algorithm, Group-Shard-Dense-Channle-Wise, is proposed in combination with the idea of grouping convolution and parameter sharing and dense connection. Based on the PeleeNet network structure, an efficient lightweight convolutional neural network, GSDCPeleeNet, is improved by using the convolution algorithm. Compared with other convolutional neural networks, this network has almost no loss of recognition accuracy or even higher recognition accuracy under the condition of fewer parameters. In this network, the step size s in the channel direction of convolution kernel in the 1×1 convolutional layer is selected as the super parameter. When the number of network parameters is smaller, better image classification effect can be achieved by adjusting and selecting the super parameter appropriately.