Fine-Grained Cat Breed Recognition with Global Context Vision Transformer
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915784157036544 |
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| author | Hera, Mowmita Parvin Kallol, Md. Shahriar Mahmud Nirob, Shohanur Rahman Bulbul, Md. Badsha Ahmed, Jubayer Islam, M. Zhourul Ali, Hazrat Bulbul, Mohammmad Farhad |
| author_facet | Hera, Mowmita Parvin Kallol, Md. Shahriar Mahmud Nirob, Shohanur Rahman Bulbul, Md. Badsha Ahmed, Jubayer Islam, M. Zhourul Ali, Hazrat Bulbul, Mohammmad Farhad |
| contents | Accurate identification of cat breeds from images is a challenging task due to subtle differences in fur patterns, facial structure, and color. In this paper, we present a deep learning-based approach for classifying cat breeds using a subset of the Oxford-IIIT Pet Dataset, which contains high-resolution images of various domestic breeds. We employed the Global Context Vision Transformer (GCViT) architecture-tiny for cat breed recognition. To improve model generalization, we used extensive data augmentation, including rotation, horizontal flipping, and brightness adjustment. Experimental results show that the GCViT-Tiny model achieved a test accuracy of 92.00% and validation accuracy of 94.54%. These findings highlight the effectiveness of transformer-based architectures for fine-grained image classification tasks. Potential applications include veterinary diagnostics, animal shelter management, and mobile-based breed recognition systems. We also provide a hugging face demo at https://huggingface.co/spaces/bfarhad/cat-breed-classifier. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_07534 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fine-Grained Cat Breed Recognition with Global Context Vision Transformer Hera, Mowmita Parvin Kallol, Md. Shahriar Mahmud Nirob, Shohanur Rahman Bulbul, Md. Badsha Ahmed, Jubayer Islam, M. Zhourul Ali, Hazrat Bulbul, Mohammmad Farhad Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing Accurate identification of cat breeds from images is a challenging task due to subtle differences in fur patterns, facial structure, and color. In this paper, we present a deep learning-based approach for classifying cat breeds using a subset of the Oxford-IIIT Pet Dataset, which contains high-resolution images of various domestic breeds. We employed the Global Context Vision Transformer (GCViT) architecture-tiny for cat breed recognition. To improve model generalization, we used extensive data augmentation, including rotation, horizontal flipping, and brightness adjustment. Experimental results show that the GCViT-Tiny model achieved a test accuracy of 92.00% and validation accuracy of 94.54%. These findings highlight the effectiveness of transformer-based architectures for fine-grained image classification tasks. Potential applications include veterinary diagnostics, animal shelter management, and mobile-based breed recognition systems. We also provide a hugging face demo at https://huggingface.co/spaces/bfarhad/cat-breed-classifier. |
| title | Fine-Grained Cat Breed Recognition with Global Context Vision Transformer |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2602.07534 |