updat the README

README.md

@@ -1,6 +1,6 @@
 ## Introduction
 
-FGVCLib is an open-source and well documented library for Fine-grained Visual Analysis. It is based on Pytorch with performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.
+FGVCLib is an open-source and well documented library for Fine-grained Visual Classification. It is based on Pytorch with performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.
 The branch works with **torch 1.12.1**, **torchvision 0.13.1**.
 
 <details open>
@@ -11,27 +11,27 @@ The branch works with **torch 1.12.1**, **torchvision 0.13.1**.
   We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.
 
 - **State of the art**
-  We implement state-of-the-art methods by the FGVCLib, [PMG](https://arxiv.org/abs/2003.03836v3), [MCL](https://arxiv.org/abs/2002.04264). 
+  We implement state-of-the-art methods by the FGVCLib, [PMG](https://arxiv.org/abs/2003.03836v3), [MCL](https://arxiv.org/abs/2002.04264), [API-Net](https://arxiv.org/abs/2002.10191), [CAL](https://ieeexplore.ieee.org/document/9710619), [TransFG](https://ieeexplore.ieee.org/document/9710619), [PIM](https://arxiv.org/abs/2202.03822). 
 
 
 ## Installation
 
-Please refer to [Installation](docs/get_started.md/#installation) for installation instructions.
+Please refer to [Installation](https://pris-cv-fgvclib.readthedocs.io/en/latest/get_started.html) for installation instructions.
 
 ## Getting started 
 
-Please see [get_started.md](docs/get_started.md) for the basic usage of FGVCLib. We provide the tutorials for:
+Please see [get_started.md](https://pris-cv-fgvclib.readthedocs.io/en/latest/get_started.html) for the basic usage of FGVCLib. We provide the tutorials for:
 
-- [with existing data existing model](docs/1_exist_data_model.md)
-- [with existing data new model](docs/2_exist_data_new_model.md)
-- [learn about apis](docs/tutorials/tutorial1_apis.md)
-- [learn about configs](docs/tutorials/tutorial2_configs.md)
-- [learn about criterions](docs/tutorials/tutorial3_criterions.md)
-- [learn about datasets](docs/tutorials/tutorial4_datasets.md)
-- [learn about metrics](docs/tutorials/tutorial5_metrics.md)
-- [learn about model](docs/tutorials/tutorial6_model.md)
-- [learn about transforms](docs/tutorials/tutorial7_transform.md)
-- [learn about the tools](docs/useful_tools.md)
+- [with existing data existing model](https://pris-cv-fgvclib.readthedocs.io/en/latest/1_exist_data_model.html)
+- [with existing data new model](https://pris-cv-fgvclib.readthedocs.io/en/latest/2_exist_data_new_model.html)
+- [learn about apis](https://pris-cv-fgvclib.readthedocs.io/en/latest/tutorials/tutorial1_apis.html)
+- [learn about configs](https://pris-cv-fgvclib.readthedocs.io/en/latest/tutorials/tutorial2_configs.html)
+- [learn about criterions](https://pris-cv-fgvclib.readthedocs.io/en/latest/tutorials/tutorial3_criterions.html)
+- [learn about datasets](https://pris-cv-fgvclib.readthedocs.io/en/latest/tutorials/tutorial4_datasets.html)
+- [learn about metrics](https://pris-cv-fgvclib.readthedocs.io/en/latest/tutorials/tutorial5_metrics.html)
+- [learn about model](https://pris-cv-fgvclib.readthedocs.io/en/latest/tutorials/tutorial6_model.html)
+- [learn about transforms](https://pris-cv-fgvclib.readthedocs.io/en/latest/tutorials/tutorial7_transform.html)
+- [learn about the tools](https://pris-cv-fgvclib.readthedocs.io/en/latest/useful_tools.html)
 
 
 </details>
@@ -54,10 +54,14 @@ Please see [get_started.md](docs/get_started.md) for the basic usage of FGVCLib.
     <tr valign="top">
       <td>
         <ul>
-            <li><a href="configs/baseline_resnet50">Baseline_ResNet50</a></li>
-            <li><a href="configs/mcl_vgg16">MCL_VGG16</a></li>
-            <li><a href="configs/pmg_resnet50">PMG_ResNet50</a></li>
-            <li><a href="configs/pmg_v2_resnet50">PMG_V2_ResNet50</a></li>
+            <li><a href="configs/resnet">Baseline_ResNet50</a></li>
+            <li><a href="configs/mutual_channel_loss">Mutual-Channel-Loss</a></li>
+            <li><a href="configs/progressive_multi_granularity_learning">PMG-ResNet50</a></li>
+            <li><a href="configs/progressive_multi_granularity_learning">PMG_V2_ResNet50</a></li>
+            <li><a href="configs/">API-Net</a></li>
+            <li><a href="configs/">CAL</a></li>
+            <li><a href="configs/">TransFG</a></li>
+            <li><a href="configs/">PIM</a></li>
       </ul>  
       </td>
       <td>
@@ -118,12 +122,29 @@ Please see [get_started.md](docs/get_started.md) for the basic usage of FGVCLib.
       </td>
       <td>
         <ul>
-            <li>Baseline</li>
-            <li><a href="configs/mcl_vgg16/README.md">MCL</a></li>
-            <li><a href="configs/pmg_resnet50/README.md">PMG</li>
-            <li>PMG_v2</li>
+            <li>Baseline_ResNet50</li>
+            <li><a href="configs/mutual_channel_loss/README.md">Mutual-Channel-Loss</a></li>
+            <li><a href="configs/progressive_multi_granularity_learning/README.md">PMG-ResNet50</a></li>
+            <li>PMG_V2_ResNet50</li>
+            <li><a href="configs/">API-Net</a></li>
+            <li><a href="configs/">CAL</a></li>
+            <li><a href="configs/">TransFG</a></li>
+            <li><a href="configs/">PIM</a></li>
       </ul>  
       </td>
     </tr>
   </tbody>
-</table>
+</table>
+
+## The Result of the SOTA
+We used fgvclib to replicate the state-of-the-art model, and the following table shows the results of our experiment. 
+
+| SOTA       | Result of the paper | Result of the official code | Result of the FGVCLib |
+| -------------------------------------------------------------------------------------- |
+| MCL        |                     |                             |                       | 
+| PMG        |                     |                             |                       |  
+| PMG-v2     |                     |                             |                       |  
+| API-Net    |        88.1         |            87.2             |         86.8          |
+| CAL        |        90.6         |            89.6             |         89.5          |
+| TransFG    |        91.7         |            91.1             |         89.3          |
+| PIM        |        92.8         |            91.9             |         91.4          |

docs/en/configs/api-net/README.md

@@ -0,0 +1,13 @@
+# API-Net
+
+[API-Net](https://arxiv.org/abs/2002.10191)
+
+## Introduction
+
+In order to effectively identify contrastive clues among highly-confused categories, the writers propose a simple but effective Attentive Pairwise Interaction Network (API-Net), which can progressively recognize a pair of fine-grained images by interaction. They aim at learning a mutual vector first to capture semantic differences in the input pair, and then comparing this mutual vector with individual vectors to highlight their semantic differences respectively. Besides, they also introduce a score-ranking regularization to promote the priorities of these features. For more details, please refer to the [paper](https://arxiv.org/abs/2002.10191).
+
+## Framework
+<div align=center>
+<img src="https://github.com/YangYuqi317/FGVCLib_docs/blob/main/src/mcl_loss.jpg?raw=true"/>
+</div>
+