To address the issue of poor recognition and classification of men's suit collar styles,a recognition and classification model based on an improved ShuffleNetV2 was proposed.Firstly,sample images of men's suits were collected to establish a sample library containing nine categories of men's suit collar styles.Secondly,the ShuffleNetV2 model was enhanced by introducing the CBAM attention mechanism and the H-Swish activation function to improve the model's feature extraction capability and effectively prevent neuron death.Additionally,transfer learning was utilized to further enhance the model's feature extraction ability,improving its recognition adaptability and accuracy.Finally,after performing data augmentation on the sample library dataset,training and validation were conducted.Comparative experimental results indicate that the improved model proposed in this paper can effectively recognize and classify men's suit collar styles,achieving an average accuracy rate of 92.82%.This paper provides an effective solution to the problem of recognizing and classifying men's suit collar styles,offering valuable insights for the custom clothing industry.
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