Transfer Learning for Turkish Cuisine Classification

Abstract

Thanks to developments in data-oriented domains like deep learning and big data, the integration of artificial intelligence with food category recognition has been a topic of interest for decades. The capacity of image classification to produce more precise outcomes in less time has made it a popular topic in computer vision. For the purpose of food categorization, three well-known CNN-based models—EfficientNetV2M, ResNet101, and VGG16—were fine-tuned in this research. Moreover, the pre-trained Vision Transformer (ViT) was used for feature extraction, followed by classification using a Random Forest (RF) algorithm. All the models were assessed on the TurkishFoods-15 dataset. It was found that the ViT and RF models were most effective in accurately capturing food images, with precision, recall, and F1-score values of 0.91, 0.86, and 0.88 respectively.