Comparison of ResNet-50 and vision transformer models for trash classification

Jiongli Liu; Jiayou Sun; Xuan Zhou

doi:10.1117/12.2671208

28 April 2023 Comparison of ResNet-50 and vision transformer models for trash classification

Jiongli Liu, Jiayou Sun, Xuan Zhou

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261021 (2023) https://doi.org/10.1117/12.2671208
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Paper, glass, fruit, scrap metal equipment, and other materials have become domestic garbage as people's living standards have dramatically improved. Plastic trash, including bags, and disposable lunch boxes, has become a global environmental threat. Carrying out waste separation and encouraging waste reduction at the source is a meaningful way to realize the quantification, harmlessness, and resourcefulness of domestic waste. Pictures contain lots of information data and many methods for successfully extracting and examining that information data. Consequently, one of the primary research projects in the field of pictures is the picture classification problem. Traditional image classification techniques can no longer process and extract information from vast amounts of image data quickly. Throughout the experiments, the accuracy of ResNet-50 is lower than ViT, most likely due to its less extensive set of parameters. And the more the parameters of the model, the higher the precision of training. Additionally, the experiment examined the performance of the ResNet-50 model with and without pretraining, and the results showed that the performance of the model without pretraining was noticeably inferior to the model with pretraining. It is better to have a model which has numerous parameters.

Citation Download Citation

Jiongli Liu, Jiayou Sun, and Xuan Zhou "Comparison of ResNet-50 and vision transformer models for trash classification", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261021 (28 April 2023); https://doi.org/10.1117/12.2671208

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KEYWORDS

Education and training

Image classification

Transformers

Performance modeling

Radon

Data modeling

Visual process modeling

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