upd md

README.md

@@ -2,64 +2,62 @@
 license: mit
 ---
 
-# Intro 简介
+# Intro
 The Chinese Traditional Instrument Sound Model is a cutting-edge research outcome based on the Chinese Traditional Instrument Sound Dataset. This model covers recordings of over 200 types of Chinese traditional musical instruments, modified instruments, and ethnic minority instruments. Notably, it includes some instruments that are rarely seen among the general public in China. By conducting in-depth analysis and learning of the sound characteristics of these instruments, the model can accurately identify and classify the sounds of various instruments, providing strong technical support for music information retrieval, musical instrument research, and the preservation and inheritance of ethnic music.
 
-中国传统乐器识别模型是一项基于中国传统乐器音响数据集训练得到的先进研究成果。该模型涵盖了 200 多种中国传统乐器、改良乐器以及少数民族乐器的录音。特别引人注目的是，这些乐器中包括了一些在中国大众中较为罕见的乐器。通过对这些乐器的声音特征进行深入分析和学习，该模型能够准确识别和分类各种乐器的声音，为音乐信息检索、乐器研究以及民族音乐的传承与保护提供了强有力的技术支持。
-
-## Demo 在线演示
+## Demo
 <https://huggingface.co/spaces/ccmusic-database/CTIS>
 
-## Usage 使用
+## Usage
 ```python
 from modelscope import snapshot_download
 model_dir = snapshot_download("ccmusic-database/CTIS")
 ```
 
-## Maintenance 维护
+## Maintenance
 ```bash
 git clone git@hf.co:ccmusic-database/CTIS
 cd CTIS
 ```
 
-## Results 训练结果
-|   Backbone    | Size(M) |     Mel     |                 CQT                  |   Chroma    |
-| :-----------: | :-----: | :---------: | :----------------------------------: | :---------: |
-|   vit_l_32    |  306.5  |    0.936    |                0.921                 | **_0.845_** |
-|    swin_t     |  28.3   | **_0.956_** |             **_0.940_**              |    0.759    |
-|               |         |             |                                      |             |
-| regnet_y_32gf |   145   | **_0.973_** | [**_0.980_**](#best-result-最佳结果) |    0.848    |
-|   vgg19_bn    |  143.7  |    0.966    |                0.965                 | **_0.852_** |
-|    alexnet    |  61.1   |    0.936    |                0.921                 |    0.661    |
-|   resnet101   |  44.5   |    0.953    |                0.949                 |    0.782    |
-| inception_v3  |  27.2   |    0.860    |                0.855                 |    0.664    |
+## Results
+|   Backbone    | Size(M) |     Mel     |             CQT             |   Chroma    |
+| :-----------: | :-----: | :---------: | :-------------------------: | :---------: |
+|   vit_l_32    |  306.5  |    0.936    |            0.921            | **_0.845_** |
+|    swin_t     |  28.3   | **_0.956_** |         **_0.940_**         |    0.759    |
+|               |         |             |                             |             |
+| regnet_y_32gf |   145   | **_0.973_** | [**_0.980_**](#best-result) |    0.848    |
+|   vgg19_bn    |  143.7  |    0.966    |            0.965            | **_0.852_** |
+|    alexnet    |  61.1   |    0.936    |            0.921            |    0.661    |
+|   resnet101   |  44.5   |    0.953    |            0.949            |    0.782    |
+| inception_v3  |  27.2   |    0.860    |            0.855            |    0.664    |
 
-### 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/CTIS/repo?Revision=master&FilePath=.%2Fregnet_y_32gf_cqt_2024-12-02_15-05-57%2Floss.jpg&View=true"></td>
+        <td><img src="https://www.modelscope.cn/models/ccmusic-database/CTIS/resolve/master/regnet_y_32gf_cqt_2024-12-02_15-05-57/loss.jpg"></td>
     </tr>
     <tr>
         <th>Training and validation accuracy</th>
-        <td><img src="https://www.modelscope.cn/api/v1/models/ccmusic-database/CTIS/repo?Revision=master&FilePath=.%2Fregnet_y_32gf_cqt_2024-12-02_15-05-57%2Facc.jpg&View=true"></td>
+        <td><img src="https://www.modelscope.cn/models/ccmusic-database/CTIS/resolve/master/regnet_y_32gf_cqt_2024-12-02_15-05-57/acc.jpg"></td>
     </tr>
     <tr>
         <th>Confusion matrix</th>
-        <td><img src="https://www.modelscope.cn/api/v1/models/ccmusic-database/CTIS/repo?Revision=master&FilePath=.%2Fregnet_y_32gf_cqt_2024-12-02_15-05-57%2Fmat.jpg&View=true"></td>
+        <td><img src="https://www.modelscope.cn/models/ccmusic-database/CTIS/resolve/master/regnet_y_32gf_cqt_2024-12-02_15-05-57/mat.jpg"></td>
     </tr>
 </table>
 
-## Dataset 数据集
+## Dataset
 <https://huggingface.co/datasets/ccmusic-database/CTIS>
 
-## Mirror 镜像
+## Mirror
 <https://www.modelscope.cn/models/ccmusic-database/CTIS>
 
-## 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},