Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (4): 743-753.DOI: 10.3778/j.issn.1673-9418.2004057

• Graphics and Image • Previous Articles     Next Articles

Improved Lightweight Network in Image Recognition

XIAO Zhenjiu, YANG Xiaodi, WEI Xian, TANG Xiaoliang   

  1. 1. College of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
    2. Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang, Fujian 362200, China
  • Online:2021-04-01 Published:2021-04-02



  1. 1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105
    2. 中国科学院海西研究院 泉州装备制造研究所,福建 晋江 362200


To solve the complexity of convolutional neural network and the large number of parameters in image recognition task, this paper proposes a lightweight network SepNet. In this structure, the traditional fully-connected layer is replaced by Kronecker product in the classifier module. In order to further optimize network structure, in the feature extraction module, by balancing the depth and width of the network, a separable residual network module using the deep separable convolution and residual network is designed. Finally, a lightweight network architecture which can realize end-to-end training is formed, which is called sep_res18_s3. The experiments are conducted on MNIST, CIFAR-10 and CIFAR-100 datasets respectively. The results show that compared with the VGG10 network, the designed SepNet can reduce the number of parameters and computation by 94.15% without losing its accuracy. At the same time, compared with cov_res18_s3, sep_res18_s3 can still reduce the parameter amount by 58.33% and 81.82% of FLOPs. Experimental results show that replacing the fully-connected layer with Kronecker product can not only maintain the accuracy of training results, but also significantly reduce the number of parameters and calculation costs, and to a certain extent, it can prevent overfitting. On this basis, combining the deep separable convolution and residual structure, it proves the effectiveness of sep_res18_s3.

Key words: image recognition, convolutional neural network (CNN), lightweight, Kronecker product, deep separable convolution



关键词: 图像识别, 卷积神经网络(CNN), 轻量化, 克罗内克积, 深度可分离卷积