基于Xception网络的弱监督细粒度图像分类

随着深度学习的快速发展,计算机视觉领域对图像的分类研究不仅仅局限于识别出物体的类别,更需要在传统图像分类任务的基础上进行更细致的类别划分.通过对现有细粒度图像分类算法和模型的分析研究,提出一种基于Xception模型与WSDAN(weakly supervised data augmentation network)弱...

Weakly Supervised Fine-Grained Image Classification Based on Xception Network

With the rapid development of deep learning, the classification of images research in the field of computer vision is not only limited to recognizing the categories of objects, but also needs more detailed classification based on the traditional image classification task. Based on the existing fine-grained image classification algorithm and model analysis, a model based on Xception model and WSDAN（weakly supervised data augmentation network） weak supervision data augment method of combination of deep learning network is applied to fine-grained image classification task. The method takes Xception network as the backbone network and feature extraction network, uses the improved WSDAN model for data augment, and feeds the augmented image back to the network as the input image to enhance the generalization ability of the network. Experiments on the commonly used fine-grained image data sets and NABirds data set show that the classification accuracy rate is 89.28%, 91.18%, 94.47%, 93.04% and 88.4%, respectively. The experimental results show that this method achieves better classification results compared with WSDAN（Pytorch） model and other mainstream fine-grained classification algorithms.