Research on Clothing Pattern Recognition and Classification Algorithm Based on Deep Learning

This study focuses on the deep learning-oriented fashion pattern recognition and classification rules, and enhances the automatic management efficiency of fashion patterns by building an efficient recognition architecture. This paper uses convolutional neural network (CNN) as the pivot model to capture the deep characteristics of fashion patterns. Generative adversarial network (GAN) is simultaneously introduced to produce many high-quality training examples, which significantly alleviates the problem of data scarcity. In addition, this paper adopts transfer learning techniques to transplant pre-trained models to fashion pattern classification tasks, aiming to accelerate model maturation and improve classification accuracy. In the model simulation stage, this paper conducts empirical tests on several public fashion pattern sets. The test results show that the proposed algorithm surpasses existing methods in core indicators such as precision, recall rate and F1 value. Specifically, the accuracy of the CNN model on the data set reached 93.5%, and after integrating the samples generated by GAN, the model accuracy jumped to 96.1%. At the same time, with the help of transfer learning, the model maturation time is reduced by about 40%. In summary, the fashion pattern recognition and classification algorithm based on deep learning presented in this study shows significant advantages in both performance and efficiency, indicating its potential application value in the field of intelligent fashion design and manufacturing.