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

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... 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.