Pyramid convolution(Pyconv)is a pyramid multi-layer structure proposed in recent years,which can extract multi-scale feature information,and it has been applied to various computer vision tasks.However,it has high redundancy and a large number of parameters.Therefore,this article proposes a lightweight pyramid convolution light-Pyconv that uses convolutional decomposition and group convolution to reduce convolutional redundancy.At the same time,residual units,channel shuffling techniques,and attention mechanisms are introduced into the design to maintain network accuracy and accelerate the extraction of effective features.On the VGG13 network,the number of parameters decreased from 1.96M to 0.56M,while the accuracy on the CIFAR-10 and CIFAR-100 datasets decreased by only 0.87%and 0.04%,respectively;On the ResNet18 network,the number of parameters decreased from 9.22M to 7.72M,while the accuracy on the two datasets only decreased by 0.24%and 0.76%,respectively.Light-Pyconv performs better than the original network structure in terms of convergence speed and accuracy fluctuations while reducing model size.
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