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.
Food classification Deep learning Convolutional neural network Image classification Transfer learning ViT
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.
Food classification Deep learning Convolutional neural network Image classification Transfer learning ViT
Primary Language | English |
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Subjects | Signal Processing |
Journal Section | Research Articles |
Authors | |
Publication Date | November 15, 2024 |
Submission Date | August 30, 2024 |
Acceptance Date | October 28, 2024 |
Published in Issue | Year 2024 |