Transfer Learning using Very Deep Pre-Trained Models for Food Image Classification

Singh, Pranjal Kumar; Susan, Seba

doi:10.1109/icccnt56998.2023.10307479

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) 2023

DOI: 10.1109/icccnt56998.2023.10307479

|View full text |Cite

Transfer Learning using Very Deep Pre-Trained Models for Food Image Classification

Pranjal Kumar Singh,

Seba Susan

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0

Philip

2024

IJIIS

View full text Add to dashboard Cite

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.

show abstract

Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0

Philip

2024

IJIIS

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Transfer Learning using Very Deep Pre-Trained Models for Food Image Classification

Cited by 1 publication

References 24 publications

Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0

Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0

Contact Info

Product

Resources

About