Much research has been done on the classification of the food101 dataset, but much of this research which achieved an accuracy score of more than 90% explores heavyweight architecture such as EfficientNetB7, Visual Geometry Group19, ResNet-200, Inception v4, DenseNet-201, ResNeXt-101, MobileNet v3 and many more. This study explores the classification of the Food101 dataset using the EfficientNetB0 architecture, a lightweight architecture. Compared to other popular CNN architecture, EfficientNetB0 has relatively small parameters, which makes it computationally efficient and suitable for deployment on resource-constraint environments. The research aims to balance model accuracy and computational efficiency, addressing the need for resource-constrained environments. Five experiments were conducted while varying the number of fine-tuned layers. Results demonstrate that the fine-tuned EfficientNetB0 model achieves an accuracy score of accuracy score of 97.54%, Top_k_categorical accuracy of 99.89%, precision of 98.21%, and recall of 97.02% in just 5 epochs. This research will significantly contribute to the field of transfer learning by developing specialized models that excel in target tasks. Besides, it will advance dietary monitoring, food logging, and health-related technologies, enabling more accessible and practical solutions for consumers. However, the optimal number of layers to fine-tune for achieving perfect accuracy with EfficientNetB0 remains uncertain. It often involves trial and error to determine the best configuration for optimal results, presenting an opportunity for future research.
| Published in | International Journal of Intelligent Information Systems (Volume 13, Issue 4) |
| DOI | 10.11648/j.ijiis.20241304.11 |
| Page(s) | 59-77 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Transfer Learning, EfficientNets, Lightweight Architecture, Convolutional Neural Network, Fine-Tuning
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APA Style
Philip, A. R. (2024). Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0. International Journal of Intelligent Information Systems, 13(4), 59-77. https://doi.org/10.11648/j.ijiis.20241304.11
ACS Style
Philip, A. R. Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0. Int. J. Intell. Inf. Syst. 2024, 13(4), 59-77. doi: 10.11648/j.ijiis.20241304.11
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Download@article{10.11648/j.ijiis.20241304.11,
author = {Adebayo Rotimi Philip},
title = {Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0
},
journal = {International Journal of Intelligent Information Systems},
volume = {13},
number = {4},
pages = {59-77},
doi = {10.11648/j.ijiis.20241304.11},
url = {https://doi.org/10.11648/j.ijiis.20241304.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241304.11},
abstract = {Much research has been done on the classification of the food101 dataset, but much of this research which achieved an accuracy score of more than 90% explores heavyweight architecture such as EfficientNetB7, Visual Geometry Group19, ResNet-200, Inception v4, DenseNet-201, ResNeXt-101, MobileNet v3 and many more. This study explores the classification of the Food101 dataset using the EfficientNetB0 architecture, a lightweight architecture. Compared to other popular CNN architecture, EfficientNetB0 has relatively small parameters, which makes it computationally efficient and suitable for deployment on resource-constraint environments. The research aims to balance model accuracy and computational efficiency, addressing the need for resource-constrained environments. Five experiments were conducted while varying the number of fine-tuned layers. Results demonstrate that the fine-tuned EfficientNetB0 model achieves an accuracy score of accuracy score of 97.54%, Top_k_categorical accuracy of 99.89%, precision of 98.21%, and recall of 97.02% in just 5 epochs. This research will significantly contribute to the field of transfer learning by developing specialized models that excel in target tasks. Besides, it will advance dietary monitoring, food logging, and health-related technologies, enabling more accessible and practical solutions for consumers. However, the optimal number of layers to fine-tune for achieving perfect accuracy with EfficientNetB0 remains uncertain. It often involves trial and error to determine the best configuration for optimal results, presenting an opportunity for future research.
},
year = {2024}
}
TY - JOUR T1 - Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0 AU - Adebayo Rotimi Philip Y1 - 2024/08/15 PY - 2024 N1 - https://doi.org/10.11648/j.ijiis.20241304.11 DO - 10.11648/j.ijiis.20241304.11 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 59 EP - 77 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20241304.11 AB - Much research has been done on the classification of the food101 dataset, but much of this research which achieved an accuracy score of more than 90% explores heavyweight architecture such as EfficientNetB7, Visual Geometry Group19, ResNet-200, Inception v4, DenseNet-201, ResNeXt-101, MobileNet v3 and many more. This study explores the classification of the Food101 dataset using the EfficientNetB0 architecture, a lightweight architecture. Compared to other popular CNN architecture, EfficientNetB0 has relatively small parameters, which makes it computationally efficient and suitable for deployment on resource-constraint environments. The research aims to balance model accuracy and computational efficiency, addressing the need for resource-constrained environments. Five experiments were conducted while varying the number of fine-tuned layers. Results demonstrate that the fine-tuned EfficientNetB0 model achieves an accuracy score of accuracy score of 97.54%, Top_k_categorical accuracy of 99.89%, precision of 98.21%, and recall of 97.02% in just 5 epochs. This research will significantly contribute to the field of transfer learning by developing specialized models that excel in target tasks. Besides, it will advance dietary monitoring, food logging, and health-related technologies, enabling more accessible and practical solutions for consumers. However, the optimal number of layers to fine-tune for achieving perfect accuracy with EfficientNetB0 remains uncertain. It often involves trial and error to determine the best configuration for optimal results, presenting an opportunity for future research. VL - 13 IS - 4 ER -
Department of Computer Science, University of Lago, Lagos, Nigeria
