FOOD IMAGE CLASSIFICATION USING VARIOUS CNN MODELS

  • Unique Paper ID: 156433
  • PageNo: 626-631
  • Abstract:
  • Food picture classification is a burgeoning area of research since it is becoming more and more significant in the health and medical fields. Deep learning models are now often employed for picture recognition and categorization. Image categorization is a common research issue in the areas of machine learning, computer vision, and image processing. There are numerous unique approaches to classifying and identifying foods that have been established in recent research papers.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{156433,
        author = {Rudraja Vansutre },
        title = {FOOD IMAGE CLASSIFICATION USING VARIOUS CNN MODELS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {3},
        pages = {626-631},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156433},
        abstract = {Food picture classification is a burgeoning area of research since it is becoming more and more significant in the health and medical fields. Deep learning models are now often employed for picture recognition and categorization. Image categorization is a common research issue in the areas of machine learning, computer vision, and image processing. There are numerous unique approaches to classifying and identifying foods that have been established in recent research papers.},
        keywords = {CNN, Deep Learning, Food-101, image classification, pre-trained CNN.},
        month = {},
        }

Cite This Article

Vansutre, R. (). FOOD IMAGE CLASSIFICATION USING VARIOUS CNN MODELS. International Journal of Innovative Research in Technology (IJIRT), 9(3), 626–631.

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