Leveraging Transfer Learning to Identify Food Categories

• Autorzy: Kolla J. V. V. , Vemula P. Ch. , Chakravarthy S. , Naidu B. S. , Patibandla D.

Identyfikatory

DOI

10.12913/22998624/142738

• Języki publikacji: EN

Abstrakty

EN

In today’s scenario, recognition of pictured food dishes automatically has significant importance. During the COVID-19 pandemic, there was a decline in people visiting restaurants for their dietary requirements. So many restaurants started offering their services online. This situation caused a demand for better categorization of food into various categories on a large scale by companies that facilitated these services. It is challenging to congregate a large dataset of food categories, so it is complex to build a generalized architecture. To solve this issue, In this paper, domain-specific transfer learning is used to build the model using some standard architectures like VGGNET, RESNET, and EFFICIENTNET family, which are trained on popular benchmark datasets such as IMAGENET, COCO, etc. The similarity between the source and target datasets is calculated to find the best source dataset, and the one with the highest similarity is chosen for transfer learning. The solution proposed in this paper outperforms some of the existing works on categorizing food items.

Słowa kluczowe

EN

convolutional neural networks; transfer learning; domain similarity; fine-tuning

PL

konwolucyjne sieci neuronowe; uczenie się przez przeniesienie; podobieństwo dziedzin; dostrajanie

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2021
• Tom: Vol. 15, no 4
• Strony: 101--109
• Opis fizyczny: Bibliogr. 30 poz., fig.

Twórcy

autor

Kolla J. V. V.

• Department of Computer Science, Gitam University, Vishakapatnam, India

autor

Vemula P. Ch.

• Department of Computer Science, Vellore Institute of Technology, Vellore, India

• https://orcid.org/0000-0002-4857-8245

autor

Chakravarthy S.

• Department of Computer Science, Gitam University, Vishakapatnam, India

autor

Naidu B. S.

• Department of Computer Science, Gitam University, Vishakapatnam, India

autor

Patibandla D.

• Department of Computer Science, Gitam University, Vishakapatnam, India

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).