ssalahmari/Saudi_Traditional_Food_Dataset

This dataset is for our paper entitled "Saudi Traditional Food Recognition Using Deep Learning."

Authors: Saeed S. Alahmari*, Alyaa Al Salem, Sarah Al-Hamammah, Aryam Qareeshah, Raghad Al-Maqboul, and Taif Al-Mansour Department of Computer Science, Najran University, Najran, Saudi Arabia *Corresponding author: ssalahmari@nu.edu.sa

The paper is under review in the Scientific Report journal.

Abstract: The use of deep learning for food recognition has attracted significant attention due to its applications in dietary monitoring, automated food logging, and nutrition analysis. While deep learning has demonstrated impressive results across various cuisines, some, including Saudi Arabian cuisine, have not been thoroughly explored. This paper examines the effectiveness of deep learning models in accurately classifying and recognizing a variety of Saudi Arabian food items. We applied convolutional neural networks (CNNs) and transformer-based architectures, taking advantage of large pre-trained models for fine-tuning on our dataset. Additionally, we have created the first dataset of traditional Saudi Arabian food, consisting of 13 categories and 3,000 images. Our approach includes data augmentation techniques and fine-tuning strategies to enhance recognition accuracy. Experimental results show that deep learning models achieve high accuracy in distinguishing among diverse food categories, even under challenging conditions such as occlusions and varying lighting. These findings underscore the potential of deep learning for real-time food recognition, contributing to advancements in health tracking, restaurant automation, and smart dietary applications.