期刊名称：Advances in Science and Technology Research Journal
印刷版ISSN：2080-4075
电子版ISSN：2299-8624
出版年度：2021
卷号：15
期号：4
页码：101-109
DOI：10.12913/22998624/142738
语种：English
出版社：Society of Polish Mechanical Engineers and Technicians
摘要：In today’s scenario, recognition of pictured food dishes automatically has signifi cant importance. During the COVID-19 pandemic, there was a decline in people visiting restaurants for their dietary requirements. So many restaurants started off ering 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-specifi c 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 fi nd 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.
