ssalahmari/Saudi_Traditional_Food

Saudi Traditional Food Recognition Using Deep Learning

Abstract: The use of deep learning for food recognition has attracted signifi- cant attention due to its applications in dietary monitoring, automated food logging, and nutrition analysis. While deep learning has demon- strated impressive results across various cuisines, some, including Saudi Arabian cuisine, have not been thoroughly explored. This paper exam- ines the effectiveness of deep learning models in accurately classifying and recognizing a variety of Saudi Arabian food items. We applied convolu- tional neural networks (CNNs) and transformer-based architectures, tak- ing advantage of large pre-trained models for fine-tuning on our dataset. Additionally, we have created the first dataset of traditional Saudi Ara- bian food, consisting of 13 categories and 3,000 images. Our approach includes data augmentation techniques and fine-tuning strategies to en- hance recognition accuracy. Experimental results show that deep learning models achieve high accuracy in distinguishing among diverse food cate- gories, 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.