Academic Journal
Image Classification for Soybean and Weeds Based on ViT
| العنوان: | Image Classification for Soybean and Weeds Based on ViT |
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| المؤلفون: | Liang, Jingxin, Wang, Dong, Ling, Xufeng |
| المصدر: | Journal of Physics: Conference Series ; volume 2002, issue 1, page 012068 ; ISSN 1742-6588 1742-6596 |
| بيانات النشر: | IOP Publishing |
| سنة النشر: | 2021 |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| DOI: | 10.1088/1742-6596/2002/1/012068 |
| DOI: | 10.1088/1742-6596/2002/1/012068/pdf |
| الاتاحة: | http://dx.doi.org/10.1088/1742-6596/2002/1/012068 https://iopscience.iop.org/article/10.1088/1742-6596/2002/1/012068 https://iopscience.iop.org/article/10.1088/1742-6596/2002/1/012068/pdf |
| Rights: | http://creativecommons.org/licenses/by/3.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining |
| رقم الانضمام: | edsbas.AEBF7D34 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1088/1742-6596/2002/1/012068 |
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