Enhancing Thai Food Classification: A CNN-Based Approach with Transfer Learning.

In this research paper, we delve into the classification of Thai cuisine images. Despite Thailand's renowned reputation for its multicultural culinary landscape, there is a noticeable gap in dedicated studies on Thai food classification. This paper seeks to fill that void by applying deep learning methodologies, specifically Convolutional Neural Networks (CNNs), to the identification of Thai cuisine. Thai cuisine, shaped by regional and intra-regional variations, serves as a powerful cultural representation for the nation. The study employs image recognition through CNN and integrates transfer learning to enhance classification performance. The collaborative learning process between CNN and transfer learning contributes to achieving a noteworthy accuracy rate of 84%. While previous research has often overlooked the specificity of Thai cuisine, our aim is to shed light on the potential of deep classification networks, offering an engaging illustration for both researchers and food enthusiasts alike contributing to the broader field of food image classification. [ABSTRACT FROM AUTHOR]