Image Classification for Soybean and Weeds Based on ViT

Autor:	Jingxin Liang, Dong Wang, Xufeng Ling
Rok vydání:	2021
Předmět:	History Contextual image classification Computer science business.industry Pattern recognition Artificial intelligence business Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 2002:012068
ISSN:	1742-6596 1742-6588
Popis:	Abstracts. In this paper, ViT deep neural network based on self-attention mechanism is used in classification for images of soybean and weeds. Firstly, the overall image is split into multiple tiles; with each tile regarded as a word, the whole image is regarded as a sentence, which can be used for image semantic recognition by natural language processing technology. We designed a ViT network with sequence length of 50, embedded dimension of 384, and self-attention module layers of 12. With soybean weed classification dataset, the network is trained, verified and tested. Experimental results showed that ViT network is superior in classification on dataset of soybean and weeds, with excellent generalization capability.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::45f755ace5a41517756cc6b92f1bc6f4 https://doi.org/10.1088/1742-6596/2002/1/012068 Zobrazit plný text záznamu