用视觉变压器精准识别猫品种，准确率超92%
Fine-Grained Cat Breed Recognition with Global Context Vision Transformer
- 采用GCViT-Tiny模型捕捉猫的全局上下文特征
- 在牛津宠物数据集上达92%测试准确率
- 适合动物识别、兽医诊断等实际场景
由于毛发图案、面部结构和颜色差异细微，从图像中准确识别猫品种是一项挑战性任务。本文基于牛津-IIIT宠物数据集的一个子集（包含多种家猫高分辨率图像），提出一种基于深度学习的猫品种分类方法，采用全局上下文视觉变压器（GCViT）-tiny架构。为提升模型泛化能力，使用了旋转、水平翻转和亮度调整等广泛数据增强策略。实验结果显示，GCViT-Tiny模型在测试集上达到92.00%的准确率，验证集准确率为94.54%。结果表明，基于Transformer的架构在细粒度图像分类任务中具有显著优势。潜在应用包括兽医诊断、动物收容所管理及移动端品种识别系统。我们还提供了Hugging Face演示链接：https://huggingface.co/spaces/bfarhad/cat-breed-classifier。
原文摘要 · Abstract (English)
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.
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