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.

翻译：从图像中准确识别猫品种是一项具有挑战性的任务，这源于毛皮花纹、面部结构和颜色等方面存在的细微差异。本文提出了一种基于深度学习的方法，利用牛津-IIIT宠物数据集的子集对猫品种进行分类，该数据集包含多种家养品种的高分辨率图像。我们采用了全局上下文视觉Transformer（GCViT）架构的微型版本（GCViT-Tiny）进行猫品种识别。为了提高模型的泛化能力，我们使用了包括旋转、水平翻转和亮度调整在内的广泛数据增强技术。实验结果表明，GCViT-Tiny模型在测试集上达到了92.00%的准确率，在验证集上达到了94.54%的准确率。这些发现凸显了基于Transformer的架构在细粒度图像分类任务中的有效性。潜在应用包括兽医诊断、动物收容所管理和基于移动设备的品种识别系统。我们还在 https://huggingface.co/spaces/bfarhad/cat-breed-classifier 上提供了一个Hugging Face演示。