关键词: 服装图像分类, ShuffleNet v1, 深度学习, 注意力机制, 非对称多尺度特征融合

Abstract:

In recent years, with the development of the Internet economy, compared with offline sales of clothing, online shopping has broken the time and geographical restrictions, and has gradually become one of the mainstream consumption methods with distinctive features such as various categories and affordable prices. According to the China E Commerce Report 2021 released by the Ministry of Commerce of the People's Republic of China, in 2021, the national online retail witnessed rapid growth, with the online retail volume reaching 13.09 trillion yuan, of which footwear and clothing products account for the largest proportion, reaching 22.94%. With the increasing demand for clothing, consumers '  requirements for clothing retrieval methods are also increasing. Consumers hope to retrieve more accurate results based on their own needs and styles. Therefore, the classification of clothing needs to be meticulous and accurate.
To promote the accurate integration of clothing retrieval results of e commerce platforms and consumer demand, it is necessary to further enrich the clothing retrieval methods of e commerce platforms. Aiming at the problem of large volume and lack of fine classification of garment image classification models, a garment image classification algorithm based on improved ShuffleNet v1 is proposed. Based on ShuffleNet v1, the algorithm reduces the computational load of the model by optimizing the number of module stacks and network layer channels to meet the real time requirements of the algorithm. Furthermore, the channel and spatial attention module is embedded to make the model focus on important feature information and suppress useless feature information. Finally, the asymmetric multi scale feature fusion module is designed to enhance the feature extraction ability of the model. The results show that the accuracy of the proposed algorithm in the self built shirt and clothing dataset is 88.31%, which is 2.77%, 3.69%, 1.98% and 0.62% higher than that of ShuffleNet v1, ShuffleNet v2, MobileNet v2 and ResNet50 models respectively. The proposed algorithm has also achieved good results in some datasets of DeepFashion, verifying the effectiveness and universality of the proposed algorithm. Compared with the basic model, the parameters of the proposed model are only 0.73M, and the parameters of the model are reduced by about 60%, which improves the accuracy and reasoning speed of the model.
This paper proposes an algorithm to achieve high precision and less time consuming shirt clothing classification, which has practical application value for helping merchants to subdivide clothing in the early stage. At the same time, it has good academic significance and reference value for the research of similar fine classification and lightweight problems. However, due to the limitation of the dataset, only a few categories of shirts such as stripes, lattices and spots are supported. In real life, a certain type of clothing has different forms and more categories. How to collect effective data or expand datasets is a direction worth exploring in the future.

Key words: clothing image classification, ShuffleNet v1, deep learning, attention mechanism, asymmetric multi scale feature fusion