关键词:  , 服装分类, MobileNetV2, 深度学习, 注意力机制, 迁移学习

Abstract: With the continuous development of Internet technology, online clothing shopping has become one of the mainstream ways for people to shop. Consumers can easily browse and purchase various types of clothing from e-commerce platforms at home, without the need to visit physical stores in person. At the same time, online shopping platforms also offer more choices and competitive prices, which is one of the main reasons why people choose to shop for clothing online. According to market research data, as of 2021, the consumption scale of online clothing shopping in China had reached 3.7 trillion yuan, accounting for nearly half of the entire online shopping market, and this number is still growing. However, quickly and accurately classifying a large number of clothing images is a challenging task. Traditional clothing classification requires a lot of time and labor to classify, sort, and label, consuming a lot of manpower and time costs. Moreover, with the annual increase in the volume of online shopping orders, traditional manual classification methods are difficult to handle large amounts of data and cannot meet the needs of rapid classification and processing. On the contrary, clothing classification methods based on deep learning can learn more features and patterns through a large amount of data and iterative training, achieving higher accuracy without human intervention. Therefore, this article proposes a clothing classification algorithm based on improved MobileNet V2.
The improved algorithm mainly embeds channel and spatial attention mechanisms into the basic unit of MobileNet V2 to form an attention mechanism basic unit, giving useful features greater weight, suppressing useless features, and enhancing the network's feature extraction ability. In addition, transfer learning is used to optimize the model parameters, and enhance the model's generalization ability and stability, so as to further improve the classification accuracy. Experimental results on the Fashion MNIST dataset show that the improved algorithm achieves an average accuracy rate of 93.16%, which is respectively 1.73%, 1.22%, 3.74%, and 3.05% higher than that of the Resnet50, Efficientnet v2_l, Shufflenet v2, and Mobilenet v2 models, effectively improving the problem of low accuracy in clothing image classification.
The algorithm proposed in this article achieves high-precision clothing classification, which not only provides consumers with a better shopping experience but also provides e-commerce platforms with more accurate clothing recommendation services, and has high application value. However, the algorithm in this study focuses on single-label clothing image classification, and there are some limitations in recognizing multiple objects within a single image. Future research will emphasize expanding the clothing image classification task to multi-label classification, and further exploring how to handle multiple clothing categories in the same image, in order to apply it to a broader range of clothing classification scenarios.

Key words: clothing classification, MobileNet v2, deep learning, attention mechanism, transfer learning