Clothing classification method based on attention mechanism and transfer learning
Aimed the low efficiency and low accuracy of clothing image classification,a clothing image classification method based on attention mechanism and transfer learning was proposed.The pre-trained ResNet50 network model was used for transfer learning on the clothing dataset to reduce the dependence on the dataset and the network training time.Image dataset was processed by data augmentation of geometric transform and color jitter to improve the generalization ability of the model.Convolutional block attention module(CBAM)was added to the ResNet50-based network,and attention of different region of clothing was improved from both channel and spatial dimensions in turn.Then the feature expression capability was enhanced.The validation was per-formed on two datasets of CD and IDFashion with different background interference.Experimen-tal results show that the proposed model can extract more clothing feature information,and the average classification accuracy in the IDFashion dataset is 95.60%,which is higher than that of ResNet50,ResNet50+STN and ResNet50+ECA models by 6.65%,6.69%,6.62%,which improves the accuracy and efficiency of clothing image classification to some extent.