Narrow Pooling Clothing Classification Based on Attention Mechanism

TP311; In recent years, with the rapid development of e-commerce, people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms. In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images, researchers have begun to exploit deep learning techniques instead of traditional learning methods. The paper explores the use of convolutional neural networks (CNNs) for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism ( HAM) that fully utilizes feature weights in three dimensions: channel, height, and width. Moreover, the improved pooling layer not only captures local feature information, but also fuses global and local information to improve the misclassification problem that occurs between similar categories. Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification (93.62％ and 67.9％) compared with residual network ( ResNet ) and convolutional block attention module(CBAM).