Thai Food Recognition Using Deep Learning With Cyclical Learning Rates

Abstract

Automated food recognition is essential in order to streamline dietary monitoring. To build and evaluate complex food recognition models, large datasets of annotated food images are crucial. In this paper, we introduce a new dataset called THFOOD-100, which is specifically designed for this purpose. This dataset consists of 53,459 high-quality images of popular Thai dishes categorized into 100 classes. We conducted a comprehensive comparison of 23 deep convolutional neural network and vision transformer architectures to establish a strong baseline for classification performance on the THFOOD-100 dataset. Additionally, we proposed training the models using cyclical learning rates, which has been shown to improve model generalization and significantly reduce training time. We demonstrated the effectiveness of cyclical learning rates with three standard optimizers on THFOOD-100, ETHZ Food-101, and UEC-Food256. The top-performing model achieved a 96% classification accuracy on THFOOD-100, showing great promise for real-world applications. Our new dataset is specifically aimed at better representing Thai cuisine in food recognition research, and our analyses offer valuable insights into the shortcomings of current models.

Keywords

• Deep learning
• food dataset
• food recognition
• image classification
• learning rate