upd md

README.md

@@ -5,61 +5,59 @@ license: mit
 # Intro 简介
 The Chinese National Pentatonic Mode Recognition Model is trained on the Chinese National Pentatonic Mode Dataset, which combines manual annotation with computational analysis. This dataset collects and annotates audio files representing the five primary tonal modes in traditional Chinese music: Gong, Shang, Jiao, Zhi, and Yu (covering five-tone, six-tone, and seven-tone scales). Detailed annotations are provided for these modes, and an in-depth analysis of the methods for identifying Chinese ethnic five-tone modes is presented. The model employs feature extraction, spectral analysis, and pattern recognition techniques to efficiently and accurately identify and classify the five-tone modes in the music. This model's application not only facilitates the digital preservation of ethnic music but also offers robust data support and a technical framework for the analysis and retrieval of ethnic music features.
 
-中国民族五声调式识别模型基于中国民族五声调式数据集进行训练，该数据集结合了人工标注与计算机分析的方法，专门收录了中国传统音乐中的五种基本调式：宫、商、角、徵、羽（涵盖五声、六声、七声音阶）。该数据集详细标注了这些调式的音频文件，并对中国民族五声调式的判别方法进行了深入分析。模型通过对音频信号进行特征提取、频谱分析以及模式识别，能够高效且准确地识别出音乐中的五声调式，并对其进行分类。该模型的应用不仅有助于民族音乐的数字化保存，还能为音乐学、民族音乐特征分析与检索提供有力的数据支持与技术框架。
-
-## Demo 在线演示
+## Demo
 <https://huggingface.co/spaces/ccmusic-database/CNPM>
 
-## Usage 使用
+## Usage
 ```python
 from modelscope import snapshot_download
 model_dir = snapshot_download("ccmusic-database/CNPM")
 ```
 
-## Maintenance 维护
+## Maintenance
 ```bash
 git clone [email protected]:ccmusic-database/CNPM
 cd CNPM
 ```
 
-## Results 训练结果
-|      Backbone      | Size(M) |     Mel     |                 CQT                  |   Chroma    |
-| :----------------: | :-----: | :---------: | :----------------------------------: | :---------: |
-|      vit_l_32      |  306.5  |    0.680    |                0.769                 |    0.399    |
-|      vit_l_16      |  304.3  | **_0.823_** | [**_0.859_**](#best-result-最佳结果) | **_0.549_** |
-|                    |         |             |                                      |             |
-|      vgg11_bn      |  132.9  | **_0.807_** |             **_0.843_**              | **_0.609_** |
-|   regnet_y_16gf    |  83.6   |    0.590    |                0.832                 |    0.535    |
-|  wide_resnet50_2   |  68.9   |    0.694    |                0.757                 |    0.531    |
-|      alexnet       |  61.1   |    0.742    |                0.744                 |    0.542    |
-| shufflenet_v2_x2_0 |   7.4   |    0.473    |                0.720                 |    0.266    |
+## Results
+|      Backbone      | Size(M) |     Mel     |             CQT             |   Chroma    |
+| :----------------: | :-----: | :---------: | :-------------------------: | :---------: |
+|      vit_l_32      |  306.5  |    0.680    |            0.769            |    0.399    |
+|      vit_l_16      |  304.3  | **_0.823_** | [**_0.859_**](#best-result) | **_0.549_** |
+|                    |         |             |                             |             |
+|      vgg11_bn      |  132.9  | **_0.807_** |         **_0.843_**         | **_0.609_** |
+|   regnet_y_16gf    |  83.6   |    0.590    |            0.832            |    0.535    |
+|  wide_resnet50_2   |  68.9   |    0.694    |            0.757            |    0.531    |
+|      alexnet       |  61.1   |    0.742    |            0.744            |    0.542    |
+| shufflenet_v2_x2_0 |   7.4   |    0.473    |            0.720            |    0.266    |
 
-### Best result 最佳结果
+### Best result
 <table>
     <tr>
         <th>Loss curve</th>
-        <td><img src="https://www.modelscope.cn/api/v1/models/ccmusic-database/CNPM/repo?Revision=master&FilePath=.%2Fvit_l_16_cqt_2024-12-03_12-31-17%2Floss.jpg&View=true"></td>
+        <td><img src="https://www.modelscope.cn/models/ccmusic-database/CNPM/resolve/master/vit_l_16_cqt_2024-12-03_12-31-17/loss.jpg"></td>
     </tr>
     <tr>
         <th>Training and validation accuracy</th>
-        <td><img src="https://www.modelscope.cn/api/v1/models/ccmusic-database/CNPM/repo?Revision=master&FilePath=.%2Fvit_l_16_cqt_2024-12-03_12-31-17%2Facc.jpg&View=true"></td>
+        <td><img src="https://www.modelscope.cn/models/ccmusic-database/CNPM/resolve/master/vit_l_16_cqt_2024-12-03_12-31-17/acc.jpg"></td>
     </tr>
     <tr>
         <th>Confusion matrix</th>
-        <td><img src="https://www.modelscope.cn/api/v1/models/ccmusic-database/CNPM/repo?Revision=master&FilePath=.%2Fvit_l_16_cqt_2024-12-03_12-31-17%2Fmat.jpg&View=true"></td>
+        <td><img src="https://www.modelscope.cn/models/ccmusic-database/CNPM/resolve/master/vit_l_16_cqt_2024-12-03_12-31-17/mat.jpg"></td>
     </tr>
 </table>
 
-## Dataset 数据集
+## Dataset
 <https://huggingface.co/datasets/ccmusic-database/CNPM>
 
-## Mirror 镜像
+## Mirror
 <https://www.modelscope.cn/models/ccmusic-database/CNPM>
 
-## Evaluation 校验
+## Evaluation
 <https://github.com/monetjoe/ccmusic_eval>
 
-## Cite 引用
+## Cite
 ```bibtex
 @dataset{zhaorui_liu_2021_5676893,
   author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},