Food Classification Model Based on Improved MobileNetV3

Jing Nan; Xiyu Lei; Xiaoyu Yang; Yifan Chang; Zhiguo Wang

doi:10.1007/978-981-99-9243-0_29

Food Classification Model Based on Improved MobileNetV3

Jing Nan, Xiyu Lei, Xiaoyu Yang, Yifan Chang, Zhiguo Wang^*

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

2 Citations (Scopus)

Abstract

Food provides people with energy and nutrition. A scientific and reasonable diet can greatly guarantee a healthy life. Food classification technology is the fundamental work of research like diet health detection. Food classification has gradually become a research hotspot in artificial intelligence field. Currently, there exists several problems in the field of food classification such as lack of public datasets, large consumption of computing resources and low classification accuracy, which are difficult to deploy on portable devices. Based on the actual needs, this paper aims at the problems in the field of food classification mentioned above and has completed the following works: Building a 102-type food dataset containing 72815 pieces of samples; Experiments were carried out based on this dataset and mainstream neural networks. Introduce Coordinate attention mechanism to improve MobileNetV3 neural network and improve classification accuracy.

Original language	English
Title of host publication	Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II
Editors	Yonghong Zhang, Lianyong Qi, Qi Liu, Guangqiang Yin, Xiaodong Liu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	287-295
Number of pages	9
ISBN (Print)	9789819992423
DOIs	https://doi.org/10.1007/978-981-99-9243-0_29
Publication status	Published - 2024
Externally published	Yes
Event	13th International Conference on Computer Engineering and Networks, CENet 2023 - Wuxi, China Duration: 3 Nov 2023 → 5 Nov 2023

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1126 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	13th International Conference on Computer Engineering and Networks, CENet 2023
Country/Territory	China
City	Wuxi
Period	3/11/23 → 5/11/23

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Attention network
Deep Learning
Efficientnetv2
Food Classification
Mobilenetv3

Access to Document

10.1007/978-981-99-9243-0_29

Cite this

Nan, J., Lei, X., Yang, X., Chang, Y., & Wang, Z. (2024). Food Classification Model Based on Improved MobileNetV3. In Y. Zhang, L. Qi, Q. Liu, G. Yin, & X. Liu (Eds.), Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II (pp. 287-295). (Lecture Notes in Electrical Engineering; Vol. 1126 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-9243-0_29

Nan, Jing ; Lei, Xiyu ; Yang, Xiaoyu et al. / Food Classification Model Based on Improved MobileNetV3. Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II. editor / Yonghong Zhang ; Lianyong Qi ; Qi Liu ; Guangqiang Yin ; Xiaodong Liu. Springer Science and Business Media Deutschland GmbH, 2024. pp. 287-295 (Lecture Notes in Electrical Engineering).

@inproceedings{869ee9f0bbe84ec988f5886ce5eef318,

title = "Food Classification Model Based on Improved MobileNetV3",

abstract = "Food provides people with energy and nutrition. A scientific and reasonable diet can greatly guarantee a healthy life. Food classification technology is the fundamental work of research like diet health detection. Food classification has gradually become a research hotspot in artificial intelligence field. Currently, there exists several problems in the field of food classification such as lack of public datasets, large consumption of computing resources and low classification accuracy, which are difficult to deploy on portable devices. Based on the actual needs, this paper aims at the problems in the field of food classification mentioned above and has completed the following works: Building a 102-type food dataset containing 72815 pieces of samples; Experiments were carried out based on this dataset and mainstream neural networks. Introduce Coordinate attention mechanism to improve MobileNetV3 neural network and improve classification accuracy.",

keywords = "Attention network, Deep Learning, Efficientnetv2, Food Classification, Mobilenetv3",

author = "Jing Nan and Xiyu Lei and Xiaoyu Yang and Yifan Chang and Zhiguo Wang",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 13th International Conference on Computer Engineering and Networks, CENet 2023 ; Conference date: 03-11-2023 Through 05-11-2023",

year = "2024",

doi = "10.1007/978-981-99-9243-0\_29",

language = "English",

isbn = "9789819992423",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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booktitle = "Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II",

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Nan, J, Lei, X, Yang, X, Chang, Y & Wang, Z 2024, Food Classification Model Based on Improved MobileNetV3. in Y Zhang, L Qi, Q Liu, G Yin & X Liu (eds), Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II. Lecture Notes in Electrical Engineering, vol. 1126 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 287-295, 13th International Conference on Computer Engineering and Networks, CENet 2023, Wuxi, China, 3/11/23. https://doi.org/10.1007/978-981-99-9243-0_29

Food Classification Model Based on Improved MobileNetV3. / Nan, Jing; Lei, Xiyu; Yang, Xiaoyu et al.
Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II. ed. / Yonghong Zhang; Lianyong Qi; Qi Liu; Guangqiang Yin; Xiaodong Liu. Springer Science and Business Media Deutschland GmbH, 2024. p. 287-295 (Lecture Notes in Electrical Engineering; Vol. 1126 LNEE).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Food Classification Model Based on Improved MobileNetV3

AU - Nan, Jing

AU - Lei, Xiyu

AU - Yang, Xiaoyu

AU - Chang, Yifan

AU - Wang, Zhiguo

PY - 2024

Y1 - 2024

N2 - Food provides people with energy and nutrition. A scientific and reasonable diet can greatly guarantee a healthy life. Food classification technology is the fundamental work of research like diet health detection. Food classification has gradually become a research hotspot in artificial intelligence field. Currently, there exists several problems in the field of food classification such as lack of public datasets, large consumption of computing resources and low classification accuracy, which are difficult to deploy on portable devices. Based on the actual needs, this paper aims at the problems in the field of food classification mentioned above and has completed the following works: Building a 102-type food dataset containing 72815 pieces of samples; Experiments were carried out based on this dataset and mainstream neural networks. Introduce Coordinate attention mechanism to improve MobileNetV3 neural network and improve classification accuracy.

AB - Food provides people with energy and nutrition. A scientific and reasonable diet can greatly guarantee a healthy life. Food classification technology is the fundamental work of research like diet health detection. Food classification has gradually become a research hotspot in artificial intelligence field. Currently, there exists several problems in the field of food classification such as lack of public datasets, large consumption of computing resources and low classification accuracy, which are difficult to deploy on portable devices. Based on the actual needs, this paper aims at the problems in the field of food classification mentioned above and has completed the following works: Building a 102-type food dataset containing 72815 pieces of samples; Experiments were carried out based on this dataset and mainstream neural networks. Introduce Coordinate attention mechanism to improve MobileNetV3 neural network and improve classification accuracy.

KW - Attention network

KW - Deep Learning

KW - Efficientnetv2

KW - Food Classification

KW - Mobilenetv3

UR - https://www.scopus.com/pages/publications/85186661042

U2 - 10.1007/978-981-99-9243-0_29

DO - 10.1007/978-981-99-9243-0_29

M3 - Conference Paper published in a book

AN - SCOPUS:85186661042

SN - 9789819992423

T3 - Lecture Notes in Electrical Engineering

SP - 287

EP - 295

BT - Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II

A2 - Zhang, Yonghong

A2 - Qi, Lianyong

A2 - Liu, Qi

A2 - Yin, Guangqiang

A2 - Liu, Xiaodong

PB - Springer Science and Business Media Deutschland GmbH

T2 - 13th International Conference on Computer Engineering and Networks, CENet 2023

Y2 - 3 November 2023 through 5 November 2023

ER -

Nan J, Lei X, Yang X, Chang Y, Wang Z. Food Classification Model Based on Improved MobileNetV3. In Zhang Y, Qi L, Liu Q, Yin G, Liu X, editors, Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II. Springer Science and Business Media Deutschland GmbH. 2024. p. 287-295. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-99-9243-0_29

Food Classification Model Based on Improved MobileNetV3

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Keywords

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