CLASSIFICATION OF ART PAINTINGS USING VISION TRANSFORMERS

Nergiz İnal; Serdar Çiftçi

Makale detayları

CLASSIFICATION OF ART PAINTINGS USING VISION TRANSFORMERS

Yazarlar:

Nergiz İnal | Serdar Çiftçi

Yayıncı:

Karabuk University

Ana Başlıklar:

Computer Vision

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.1-16

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350061

pISSN:

2980-3152

Anahtar kelimeler:

Özet:

 In this study, we investigated the performance of art painting classification based on the categories of Genre, Artist, and Style using deep learning methods. We employed convolutional neural network CNN-based models such as ResNet, MobileNet, EfficientNet, and ConvNeXt for their effectiveness in feature extraction. Additionally, we used vision transformer models, including ViT, Swin, BEiT, and DeiT, which used attention mechanisms. We conducted our experiments on the publicly available WikiArt dataset, and the BEiT model achieved the highest classification accuracy in the Artist and Genre categories, with results of 84.90 and 79.52, respectively. In Style category, the Swin model produced the best result with an accuracy of 72.59. In general, our findings indicate that transformer-based methods outperformed CNN-based methods. Furthermore, we compared our results with similar studies in the literature and showed that transformer-based models generally perform better in classifying art paintings.

 In this study, we investigated the performance of art painting classification based on the categories of Genre, Artist, and Style using deep learning methods. We employed convolutional neural network CNN-based models such as ResNet, MobileNet, EfficientNet, and ConvNeXt for their effectiveness in feature extraction. Additionally, we used vision transformer models, including ViT, Swin, BEiT, and DeiT, which used attention mechanisms. We conducted our experiments on the publicly available WikiArt dataset, and the BEiT model achieved the highest classification accuracy in the Artist and Genre categories, with results of 84.90 and 79.52, respectively. In Style category, the Swin model produced the best result with an accuracy of 72.59. In general, our findings indicate that transformer-based methods outperformed CNN-based methods. Furthermore, we compared our results with similar studies in the literature and showed that transformer-based models generally perform better in classifying art paintings.

 In this study, we investigated the performance of art painting classification based on the categories of Genre, Artist, and Style using deep learning methods. We employed convolutional neural network CNN-based models such as ResNet, MobileNet, EfficientNet, and ConvNeXt for their effectiveness in feature extraction. Additionally, we used vision transformer models, including ViT, Swin, BEiT, and DeiT, which used attention mechanisms. We conducted our experiments on the publicly available WikiArt dataset, and the BEiT model achieved the highest classification accuracy in the Artist and Genre categories, with results of 84.90 and 79.52, respectively. In Style category, the Swin model produced the best result with an accuracy of 72.59. In general, our findings indicate that transformer-based methods outperformed CNN-based methods. Furthermore, we compared our results with similar studies in the literature and showed that transformer-based models generally perform better in classifying art paintings.

 In this study, we investigated the performance of art painting classification based on the categories of Genre, Artist, and Style using deep learning methods. We employed convolutional neural network CNN-based models such as ResNet, MobileNet, EfficientNet, and ConvNeXt for their effectiveness in feature extraction. Additionally, we used vision transformer models, including ViT, Swin, BEiT, and DeiT, which used attention mechanisms. We conducted our experiments on the publicly available WikiArt dataset, and the BEiT model achieved the highest classification accuracy in the Artist and Genre categories, with results of 84.90 and 79.52, respectively. In Style category, the Swin model produced the best result with an accuracy of 72.59. In general, our findings indicate that transformer-based methods outperformed CNN-based methods. Furthermore, we compared our results with similar studies in the literature and showed that transformer-based models generally perform better in classifying art paintings.

Makale detayları

CLASSIFICATION OF ART PAINTINGS USING VISION TRANSFORMERS

Yazarlar:

Nergiz İnal | Serdar Çiftçi

Yayıncı:

Karabuk University

Ana Başlıklar:

Computer Vision

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.1-16

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350061

pISSN:

2980-3152

Painting Classifications

Vision Transformers

Transfer Learning

Sayı

Makale detayları

CLASSIFICATION OF ART PAINTINGS USING VISION TRANSFORMERS

Yazarlar: Nergiz İnal | Serdar Çiftçi

Yayıncı: Karabuk University

Ana Başlıklar: Computer Vision

Dergi: Current Trends in Computing Vol.3 Issue 1, pp.1-16

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001350061

pISSN: 2980-3152

Painting Classifications

Vision Transformers

Transfer Learning

Sayı

Yazarlar:

Nergiz İnal | Serdar Çiftçi

Yayıncı:

Karabuk University

Ana Başlıklar:

Computer Vision

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.1-16

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350061

pISSN:

2980-3152