Academic Journal of Computing & Information Science, 2024, 7(8); doi: 10.25236/AJCIS.2024.070802.
Huimin Lu1, Handong Song2
1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
2School of Mechanical Engineering, University of Jinan, Jinan, 250024, China
As one of the representative algorithms of convolutional neural networks, deep residual network has achieved very good results in computer vision, classification and other fields. Deep residual networks redefine the way networks learn, allowing networks to directly learn the difference between input and output information. This paper takes the deep residual network as the main research object, introduces the innovation of the deep residual network, and uses the deep residual network and other neural network models to train on different datasets. It is found that the highest accuracy of the test set of the deep residual network on the MNIST dataset is 0.555% higher than that of LeNet. 0.225% higher than AlexNet; The maximum accuracy of the test set on the CIFAR-10 dataset is 23.13% higher than LeNet and 2.46% higher than AlexNet.
Deep residual network, Convolutional neural network, Image recognition, Neural network model
Huimin Lu, Handong Song. Research on image classification based on ResNet. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 8: 9-14. https://doi.org/10.25236/AJCIS.2024.070802.
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