Transfer Learning for Turkish Cuisine Classification

Sait Alp

Makale detayları

Transfer Learning for Turkish Cuisine Classification

Yazarlar:

Sait Alp

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Sinyal İşleme

Dergi:

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

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330284

pISSN:

2619-8991

Anahtar kelimeler:

Özet:

 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.

Makale detayları

Transfer Learning for Turkish Cuisine Classification

Yazarlar:

Sait Alp

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Sinyal İşleme

Dergi:

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

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

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

Sayı

Makale detayları

Yazarlar: Sait Alp

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: Sinyal İşleme

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

Belge Tipi: Makale

Yayın Tarihi: 11/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001330284

pISSN: 2619-8991

Sayı

Yazarlar:

Sait Alp

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Sinyal İşleme

Dergi:

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

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330284

pISSN:

2619-8991