In embedded systems and edge computing,in order to improve the computational efficiency of the VGG16 Convolutional Neural Networks for small-size image recognition,the VGG16 network is improved by adjusting the number of fully connected layers and the number of convolutional kernels in the model,using global average pooling to replace fully connected layers,and other ways,so as to reduce the number of trainable parameters of the network model.The improved neural network model is trained on the CIFAR-10 dataset with image enhancement.The recognition accuracy of the training set reaches more than 99%,and the recognition accuracy of test set can reach more than 90%.The number of parameters of the improved network model is reduced by 89.04%compared with the VGG16 network,which verifies the effectiveness of the improved network model.
维普
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