A New Efficient and Lightweight Convolutional Neural Network Model

Abstract

Abstract: Aiming at the problem that the current food recognition system has a large number of network model parameters and a large model, this paper proposes a 23-layer network model with only 204k parameters. The basic building blocks (7×7, 5×5, 3×3) are used to generate feature maps, and two pooling layers of different receptive fields are used to fuse the feature map of the convolutional layer, and a 1×1 convolution kernel is used for nonlinear combination. Then it is connected to the spatial pyramid pooling layer, and finally is classified in the softmax classifier. Experiments on public data sets show that, compared with ResNet50 and GoogLeNet, the network model in this paper reduces model parameters by 99.14% and 96.63% respectively without reducing classification performance.

Key words: convolutional neural network, deep learning, food classification, spatial pyramid pooling

ZHANG Jian-jian. A New Efficient and Lightweight Convolutional Neural Network Model[J]. Computer and Modernization, 2021, 0(04): 98-103.

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