Food Image Classification Based on CBAM-Inception V3 Transfer Learning
To improve the accuracy of automatic recognition and classification of food images, a classification model CBAM- InceptionV3 is proposed, which embeds the Convolutional Block Attention Module. The specific method is to split the Inception V3 model with ImageNet pre-trained weight parameters into blo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academy of National Food and Strategic Reserves Administration
2024-01-01
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| Series: | Liang you shipin ke-ji |
| Subjects: | |
| Online Access: | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20240113 |
| _version_ | 1797347152924508160 |
|---|---|
| author | DU Hui-jiang CUI Xiao-yi WANG Yi-meng SUN Li-ping |
| author_facet | DU Hui-jiang CUI Xiao-yi WANG Yi-meng SUN Li-ping |
| author_sort | DU Hui-jiang |
| collection | DOAJ |
| description | To improve the accuracy of automatic recognition and classification of food images, a classification model CBAM- InceptionV3 is proposed, which embeds the Convolutional Block Attention Module. The specific method is to split the Inception V3 model with ImageNet pre-trained weight parameters into blocks, embed CBAM modules after each Inception block, and reassemble them into a new model, embedding a total of 11 CBAM modules. This new model is used for transfer learning of Food-101 food image dataset padded and scaled to 299 pixels in both length and width, with the highest accuracy of 82.01%. Compared with the original Inception V3 model, the CBAM module can effectively improve the model's feature extraction and classification capabilities. At the same time, transfer learning can significantly improve the accuracy rate and shorten the training time compared with the training from scratch. Compared with several other mainstream convolutional neural network models, the results show that this new model has higher recognition accuracy and can provide strong support for food image classification and recognition. |
| first_indexed | 2024-03-08T11:43:33Z |
| format | Article |
| id | doaj.art-8610d15e5cb140358d6032700d19530c |
| institution | Directory Open Access Journal |
| issn | 1007-7561 |
| language | English |
| last_indexed | 2024-03-08T11:43:33Z |
| publishDate | 2024-01-01 |
| publisher | Academy of National Food and Strategic Reserves Administration |
| record_format | Article |
| series | Liang you shipin ke-ji |
| spelling | doaj.art-8610d15e5cb140358d6032700d19530c2024-01-25T02:05:16ZengAcademy of National Food and Strategic Reserves AdministrationLiang you shipin ke-ji1007-75612024-01-01321919810.16210/j.cnki.1007-7561.2024.01.012Food Image Classification Based on CBAM-Inception V3 Transfer LearningDU Hui-jiang0CUI Xiao-yi1WANG Yi-meng2SUN Li-ping3School of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, ChinaSchool of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, ChinaSchool of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, ChinaSchool of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, ChinaTo improve the accuracy of automatic recognition and classification of food images, a classification model CBAM- InceptionV3 is proposed, which embeds the Convolutional Block Attention Module. The specific method is to split the Inception V3 model with ImageNet pre-trained weight parameters into blocks, embed CBAM modules after each Inception block, and reassemble them into a new model, embedding a total of 11 CBAM modules. This new model is used for transfer learning of Food-101 food image dataset padded and scaled to 299 pixels in both length and width, with the highest accuracy of 82.01%. Compared with the original Inception V3 model, the CBAM module can effectively improve the model's feature extraction and classification capabilities. At the same time, transfer learning can significantly improve the accuracy rate and shorten the training time compared with the training from scratch. Compared with several other mainstream convolutional neural network models, the results show that this new model has higher recognition accuracy and can provide strong support for food image classification and recognition.http://lyspkj.ijournal.cn/lyspkj/article/abstract/20240113food image classificationchannel attention; spatial attentioncbaminceptionv3transfer learning |
| spellingShingle | DU Hui-jiang CUI Xiao-yi WANG Yi-meng SUN Li-ping Food Image Classification Based on CBAM-Inception V3 Transfer Learning Liang you shipin ke-ji food image classification channel attention; spatial attention cbam inceptionv3 transfer learning |
| title | Food Image Classification Based on CBAM-Inception V3 Transfer Learning |
| title_full | Food Image Classification Based on CBAM-Inception V3 Transfer Learning |
| title_fullStr | Food Image Classification Based on CBAM-Inception V3 Transfer Learning |
| title_full_unstemmed | Food Image Classification Based on CBAM-Inception V3 Transfer Learning |
| title_short | Food Image Classification Based on CBAM-Inception V3 Transfer Learning |
| title_sort | food image classification based on cbam inception v3 transfer learning |
| topic | food image classification channel attention; spatial attention cbam inceptionv3 transfer learning |
| url | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20240113 |
| work_keys_str_mv | AT duhuijiang foodimageclassificationbasedoncbaminceptionv3transferlearning AT cuixiaoyi foodimageclassificationbasedoncbaminceptionv3transferlearning AT wangyimeng foodimageclassificationbasedoncbaminceptionv3transferlearning AT sunliping foodimageclassificationbasedoncbaminceptionv3transferlearning |