Abstract

To establish a Peking opera costume database and preserve the cultural heritage of opera costumes,  an improved ResNet-18-based classification model (ResNet18-APSS) for classifying Peking opera Dan costumes is proposed, addressing the issue of classification accuracy. First, Peking opera Dan costume sample images were collected to construct a dataset comprising 18 categories. Then, the pre-trained ResNet-18 model was modified by replacing the fourth convolutional layer with an APSS module. Hyperparameter optimization was performed using Optuna to find the optimal configuration. Transfer learning and automatic mixed precision training strategies were employed to enhance model performance further, and the loss function was adjusted to CrossEntropyLoss to accelerate convergence. Finally, the identified optimal hyperparameters were used for training and validation. Comparative results on the self-constructed Peking opera Dan costume dataset demonstrate that the improved model effectively classifies Peking opera Dan costumes, achieving an average accuracy of 95.41%. This study provides an effective solution to the classification challenges of Peking opera Dan costumes and significantly contributes to advancing their digitization.

This work is licensed under a Creative Commons Attribution 4.0 License.