基于深度模型迁移的细粒度图像分类方法

针对细粒度图像分类方法中存在模型复杂度较高、难以利用较深模型等问题，提出深度模型迁移（DMT）分类方法。首先，在粗粒度图像数据集上进行深度模型预训练；然后，使用细粒度图像数据集对预训练模型logits层进行不确切监督学习，使其特征分布向新数据集特征分布方向迁移；最后，将迁移模型导出，在对应的测试集上进行测试。实验结果表明，在STANFORD DOGS、CUB-200-2011、OXFORD FLOWER-102细粒度图像数据集上，DMT分类方法的分类准确率分别达到72.23%、73.33%和96.27%，验证了深度模型迁移方法在细粒度图像分类领域的有效性。

Fine-grained image classification method based on deep model transfer

To solve the problems of fine-grained image classification methods, such as highly complex methods and difficulty of using deeper models, a Deep Model Transfer (DMT) method was proposed. Firstly, the deep model was pre-trained on the coarse-grained image dataset. Secondly, the pre-trained deep model classification layer was trained based on inexact supervised learning by using fine-grained image dataset and transferred to the feature distribution direction of the novel dataset. Finally, the trained model was exported and tested on the corresponding test sets. The experimental results show that the classification accuracy rates on the STANFORD DOGS, CUB-200-2011 and OXFORD FLOWER-102 fine-grained image datasets are 72.23%, 73.33%, and 96.27%, respectively, which proves the effectiveness of DMT method on fine-grained image classification.