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A comprehensive dataset for Bangladeshi dessert classification.

Mushfiqur Rahman1, Jahid Hasan1

  • 1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

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|November 25, 2024
PubMed
Summary

This study introduces a new dataset for classifying Bangladeshi desserts using deep learning. The developed models achieved 98% accuracy, aiding culinary heritage preservation.

Keywords:
Bangladeshi cuisineDeep learningDessert classificationImage datasetImage processing computer vision

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Area of Science:

  • Computer Science
  • Food Science
  • Cultural Heritage Studies

Background:

  • Classifying desserts is complex due to diverse culinary traditions.
  • A comprehensive dataset for Bangladeshi desserts is lacking.
  • Automated classification can aid in cultural heritage preservation.

Purpose of the Study:

  • To create a high-quality image dataset of traditional Bangladeshi desserts.
  • To develop and evaluate deep learning models for Bangladeshi dessert classification.
  • To establish a benchmark for culinary image recognition.

Main Methods:

  • Curated a diverse collection of high-resolution images of Bangladeshi desserts.
  • Employed image processing techniques and deep learning algorithms, including MobileNet.
  • Evaluated model performance using standardized metrics.

Main Results:

  • Achieved an overall test accuracy of 98% for dessert classification.
  • Demonstrated the effectiveness of deep learning models on the curated dataset.
  • Established a valuable resource for culinary image classification research.

Conclusions:

  • The developed dataset and models offer a significant advancement in classifying Bangladeshi desserts.
  • This work supports the preservation of culinary heritage through technological applications.
  • The findings encourage further research in machine learning for cultural applications.