Aiming at the problem of low recognition rate of MnasNet network on low-resolution image datasets

e. g. CIFAR—10

propose a lightweight convolutional bottleneck block based on pyramid structure for building a novel PSMnasNet network. Firstly

adjust the downsampling in some bottleneck blocks what based on the resolution of the image. Then

utilize the spatial pyramid pooling method to construct the pyramid structure bottleneck（ PSBottleneck） block. Finally

hyperparametric beta is embedded into the PSBottleneck to control the spatial complexity and time complexity. The experimental results in the CINIC—10 dataset show that the PSMnasNet network consisting of the PSBottleneck has a parameter reduction of approximately 22. 3 % compared to the original MnasNet network. At the same time

the classification precicion of the network is increased by nearly 13. 3 %.