Comparative analysis on different convolutional neural network (CNN) for classification
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
CNN is one of the representative algorithms of deep learning. With the development of theory and the improvement of numerical computing equipment, CNN has developed rapidly. A variety of models have been derived and applied to different places. Taking the classification of vegetables and fruits as the data reference, this paper studies three CNN models: AlexNet, VGG and NIN. Our purpose is to analyze which model is most suitable for this image classification task and compare the advantages and disadvantages of the three models. By adjusting different hyper parameters; relative learning rate (Lr), epochs(#epochs) and batch size(batch _size) on the training loss, train accuracy and test accuracy among three models. The VGGNet with Lr = 0.00001, #epochs = 80 and batch_size = 32 has the best qualified for this graphic classification task.
Publication Title
2022 IEEE 5th International Conference on Information Systems and Computer Aided Education, ICISCAE 2022
First Page Number
598
Last Page Number
601
DOI
10.1109/ICISCAE55891.2022.9927565
Recommended Citation
Zeng, Rui and Zhang, Yunfan, "Comparative analysis on different convolutional neural network (CNN) for classification" (2022). Kean Publications. 704.
https://digitalcommons.kean.edu/keanpublications/704