基于轻量化卷积神经网络的服装分类方法

考虑到电商平台的日益发展，使用人工分类的方式对服装进行分类无法满足目前的需求.本文从实际的应用场景出发，针对于服装图像进行分类时会受到背景因素干扰、服装图像关键部位信息以及算法模型运行的的硬件要求三个方面，分别进行改进设计.提出：1）消除背景的干扰；2）图像局部信息的利用；3）模型的轻量化处理.最终得到了在满足准确性的前提下，可以在普通低配置PC端进行运行的算法模型，提升了工作效率，同时节省了成本.

Clothing Classification Method Based on Lightweight Convolutional Neural Network

Considering the growing development of e-commerce platforms, the use of artificial classification of clothing classification cannot meet the current needs. In this study, based on the actual application scenarios, the design is improved in three aspects:the interference of background factors, the key position information of the garment image, and the hardware requirements of the algorithm model operation when classifying the garment image. Accordingly, it is proposed that to remove background interference, to use of local information of images, and to lightweight processing of the model. Finally, on the premise of satisfying the accuracy, the algorithm model that can be operated in the ordinary low-configuration PC terminal is obtained, which improves the work efficiency and saves the cost.