- Tytuł:
- Leveraging Transfer Learning to Identify Food Categories
- Autorzy:
-
Kolla, J. V. V.
Vemula, P. Ch.
Chakravarthy, S.
Naidu, B. S.
Patibandla, D. - Powiązania:
- https://bibliotekanauki.pl/articles/2023418.pdf
- Data publikacji:
- 2021
- Wydawca:
- Stowarzyszenie Inżynierów i Techników Mechaników Polskich
- Tematy:
-
convolutional neural networks
transfer learning
domain similarity
fine-tuning
konwolucyjne sieci neuronowe
uczenie się przez przeniesienie
podobieństwo dziedzin
dostrajanie - Opis:
- 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.
- Źródło:
-
Advances in Science and Technology. Research Journal; 2021, 15, 4; 101-109
2299-8624 - Pojawia się w:
- Advances in Science and Technology. Research Journal
- Dostawca treści:
- Biblioteka Nauki
Artykuł