Transfer Learning for Turkish Cuisine Classification

Sait Alp

Article Details

Transfer Learning for Turkish Cuisine Classification

Authors:

Sait Alp

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Sinyal İşleme

Journal:

Black Sea Journal of Engineering and Science Vol.7 Issue 6, pp.1302-1309

Document Type:

Article

Publication Date:

11/2024

Document Language:

English

Document ID (Org No.):

2001330284

pISSN:

2619-8991

Keywords:

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 modelsEfficientNetV2M, ResNet101, and VGG16were 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.

 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 modelsEfficientNetV2M, ResNet101, and VGG16were 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.

 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 modelsEfficientNetV2M, ResNet101, and VGG16were 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.

 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 modelsEfficientNetV2M, ResNet101, and VGG16were 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.

Article Details

Transfer Learning for Turkish Cuisine Classification

Authors:

Sait Alp

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Sinyal İşleme

Journal:

Black Sea Journal of Engineering and Science Vol.7 Issue 6, pp.1302-1309

Document Type:

Article

Publication Date:

11/2024

Document Language:

English

Document ID (Org No.):

2001330284

pISSN:

2619-8991

Food classification

Deep learning

Convolutional neural network

Image classification

Transfer learning

ViTFood classification

Deep learning

Convolutional neural network

Image classification

Transfer learning

ViT

Issues

Article Details

Authors: Sait Alp

Publisher: Karyay Karadeniz Yayımcılık

Main Topics: Sinyal İşleme

Journal: Black Sea Journal of Engineering and Science Vol.7 Issue 6, pp.1302-1309

Document Type: Article

Publication Date: 11/2024

Document Language: English

Document ID (Org No.): 2001330284

pISSN: 2619-8991

Issues

Authors:

Sait Alp

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

Sinyal İşleme

Journal:

Black Sea Journal of Engineering and Science Vol.7 Issue 6, pp.1302-1309

Document Type:

Article

Publication Date:

11/2024

Document Language:

English

Document ID (Org No.):

2001330284

pISSN:

2619-8991