纺织工程学报2024，Vol.2Issue(5)：51-62,12.
基于改进ResNet50和迁移学习的服饰分类识别
Research on apparel classification recognition based on improved ResNet50 and transfer learning
摘要
Abstract
Traditional apparel classification methods mainly rely on extracting features such as texture,color,and edge of the image,which is a cumbersome process and has low classification accuracy.In order to improve the performance of apparel category classification,an apparel classification and recognition method based on im-proved ResNet50 and migration learning is proposed.Firstly,two average pooling layers with pooling kernel size of 2×2 and step size of 2 are added to STAGE5 of ResNet50 network together with a convolutional layer with convolutional kernel size of 1×1 and step size of 1.Secondly,the Convolutional Block Attention Module(CBAM)is fused behind the last convolutional layer.these two improvement methods make it possible to re-duce the dimension of the feature map while retaining more information,which improves the performance of the model;lastly,a migration learning method is used to migrate the trained weights on the ImageNet dataset to the improved network,and the network is fine-tuned and validated using the dress image dataset.The results show that the accuracy of the improved ResNet50 network is up to 90%,which is 2.5%,0.4%,and 0.1%higher than the original ResNet50 in Top1,Top3,and Top5 classification accuracy,respectively.Meanwhile,it has high-er accuracy than the existing four classical convolutional neural networks(GoogleNet,VGG-16,MobileNet_v2,AlexNet),which verifies the superiority of this model in the field of apparel image classification and recognition.关键词
服饰图像分类/注意力机制/ResNet50网络/迁移学习/卷积神经网络Key words
apparel image classification/attention mechanism/ResNet50 network/transfer learning/convolu-tional neural network分类
轻工纺织引用本文复制引用
郑兴任,袁子厚,杜焱铭,张红伟..基于改进ResNet50和迁移学习的服饰分类识别[J].纺织工程学报,2024,2(5):51-62,12.基金项目
国家自然科学基金(11502177) （11502177）
湖北省数字化纺织装备重点实验室开放基金项目(DTL2019019). （DTL2019019）
