Research on Dan Character Peking Opera Costume Classification Based on Improved ResNet-18
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".