西安工程大学学报2024，Vol.38Issue(3)：109-116,8.DOI:10.13338/j.issn.1674-649x.2024.03.015
基于注意力机制和迁移学习的服装分类方法
Clothing classification method based on attention mechanism and transfer learning
摘要
Abstract
Aimed the low efficiency and low accuracy of clothing image classification,a clothing image classification method based on attention mechanism and transfer learning was proposed.The pre-trained ResNet50 network model was used for transfer learning on the clothing dataset to reduce the dependence on the dataset and the network training time.Image dataset was processed by data augmentation of geometric transform and color jitter to improve the generalization ability of the model.Convolutional block attention module(CBAM)was added to the ResNet50-based network,and attention of different region of clothing was improved from both channel and spatial dimensions in turn.Then the feature expression capability was enhanced.The validation was per-formed on two datasets of CD and IDFashion with different background interference.Experimen-tal results show that the proposed model can extract more clothing feature information,and the average classification accuracy in the IDFashion dataset is 95.60%,which is higher than that of ResNet50,ResNet50+STN and ResNet50+ECA models by 6.65%,6.69%,6.62%,which improves the accuracy and efficiency of clothing image classification to some extent.关键词
服装图像分类/ResNet50/卷积注意力机制模块(CBAM)/注意力机制/迁移学习Key words
clothing classification/ResNet50/convolutional block attention module/attention mechanism/transfer learning分类
信息技术与安全科学引用本文复制引用
陈金广,黄晓菊,马丽丽..基于注意力机制和迁移学习的服装分类方法[J].西安工程大学学报,2024,38(3):109-116,8.基金项目
陕西省自然科学基础研究计划项目(2023-JC-YB-568) （2023-JC-YB-568）
陕西省教育厅科研计划项目(22JP028) （22JP028）
