diff --git a/README.md b/README.md
index 65eb7933b62836b01e9b3fc23a83b3312772ccdc..fd13143f967ae15a1dd02652145adab90a19a2d6 100755
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ PaddleSlim支持以下功能，也支持自定义量化、裁剪等功能。
   <th><a href="https://github.com/PaddlePaddle/PaddleSlim/blob/release/2.0.0/docs/zh_cn/tutorials/quant/overview.md">Quantization</a></th>
   <th><a href="https://github.com/PaddlePaddle/PaddleSlim/blob/release/2.0.0/docs/zh_cn/tutorials/pruning/overview.md">Pruning</a></th>
   <th><a href="https://github.com/PaddlePaddle/PaddleSlim/blob/release/2.0.0/docs/zh_cn/tutorials/nas/overview.md">NAS</a></th>
-  <th><a href="">Distilling</a></th>
+  <th><a href="https://github.com/PaddlePaddle/PaddleSlim/tree/release/2.0.0/docs/zh_cn/tutorials">Distilling</a></th>
 </tr>
 <tr valign="top">
   <td>
diff --git a/README_en.md b/README_en.md
index 0c46a65fce02508ad26ae0eb9b483bfb08ac47fd..18d2cbc9b5e25d592e3082ab3e7d71a5fe646693 100755
--- a/README_en.md
+++ b/README_en.md
@@ -96,7 +96,7 @@ pip install paddleslim==1.2.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
 
 - [Algorithm Background](https://paddleslim.readthedocs.io/en/latest/intro_en.html): Introduce the background of quantization, pruning, distillation, NAS.
 
-- [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/master/slim): Introduce how to use PaddleSlim in PaddleDetection library.
+- [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim): Introduce how to use PaddleSlim in PaddleDetection library.
 
 - [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg/tree/develop/slim): Introduce how to use PaddleSlim in PaddleSeg library.
 
@@ -112,9 +112,9 @@ Dataset: ImageNet2012; Model: MobileNetV1;
 
 |Method |Accuracy(baseline: 70.91%) |Model Size(baseline: 17.0M)|
 |:---:|:---:|:---:|
-| Knowledge Distillation(ResNet50)| [+1.06%]() |-|
-| Knowledge Distillation(ResNet50) + int8 quantization |[+1.10%]()| [-71.76%]()|
-| Pruning(FLOPs-50%) + int8 quantization|[-1.71%]()|[-86.47%]()|
+| Knowledge Distillation(ResNet50)| +1.06% |-|
+| Knowledge Distillation(ResNet50) + int8 quantization |+1.10%| -71.76%|
+| Pruning(FLOPs-50%) + int8 quantization|-1.71%|-86.47%|
 
 
 ### Object Detection
@@ -123,17 +123,17 @@ Dataset: ImageNet2012; Model: MobileNetV1;
 
 |        Method           | mAP(baseline: 76.2%)         | Model Size(baseline: 94MB)      |
 | :---------------------:   | :------------: | :------------:|
-| Knowledge Distillation(ResNet34-YOLOv3) | [+2.8%]()      |       -       |
-| Pruning(FLOPs -52.88%)        | [+1.4%]()      | [-67.76%]()   |
-|Knowledge DistillationResNet34-YOLOv3)+Pruning(FLOPs-69.57%)| [+2.6%]()|[-67.00%]()|
+| Knowledge Distillation(ResNet34-YOLOv3) | +2.8%      |       -       |
+| Pruning(FLOPs -52.88%)        | +1.4%      | -67.76%   |
+|Knowledge DistillationResNet34-YOLOv3)+Pruning(FLOPs-69.57%)| +2.6%|-67.00%|
 
 
 #### Dataset: COCO; Model: MobileNet-V1-YOLOv3
 
 |        Method           | mAP(baseline: 29.3%) | Model Size|
 | :---------------------:   | :------------: | :------:|
-| Knowledge Distillation(ResNet34-YOLOv3) |  [+2.1%]()     |-|
-| Knowledge Distillation(ResNet34-YOLOv3)+Pruning(FLOPs-67.56%) | [-0.3%]() | [-66.90%]()|
+| Knowledge Distillation(ResNet34-YOLOv3) |  +2.1%     |-|
+| Knowledge Distillation(ResNet34-YOLOv3)+Pruning(FLOPs-67.56%) | -0.3% | -66.90%|
 
 ### NAS
 
@@ -141,6 +141,6 @@ Dataset: ImageNet2012; Model: MobileNetV2
 
 |Device           | Infer time cost | Top1 accuracy(baseline:71.90%) |
 |:---------------:|:---------:|:--------------------:|
-| RK3288  | [-23%]()    | +0.07%    |
-| Android cellphone  | [-20%]()    | +0.16% |
-| iPhone 6s   | [-17%]()    | +0.32%  |
+| RK3288  | -23%    | +0.07%    |
+| Android cellphone  | -20%    | +0.16% |
+| iPhone 6s   | -17%    | +0.32%  |
diff --git a/demo/detection/README.md b/demo/detection/README.md
index a48853069ca3f9eaf2e03c2a30d8bb7ce27fb36e..32160d635bb858b7d1f7d1e8b7add2e2ef38b07a 100644
--- a/demo/detection/README.md
+++ b/demo/detection/README.md
@@ -83,7 +83,7 @@
 
 ### 蒸馏通道剪裁模型
 
-可通过高精度模型蒸馏通道剪裁后模型的方式，训练方法及相关示例见[蒸馏通道剪裁模型](https://github.com/PaddlePaddle/PaddleDetection/blob/master/slim/extensions/distill_pruned_model/distill_pruned_model_demo.ipynb)。
+可通过高精度模型蒸馏通道剪裁后模型的方式，训练方法及相关示例见[蒸馏通道剪裁模型](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/static/slim/extensions/distill_pruned_model/distill_pruned_model_demo.ipynb)。
 
 COCO数据集上蒸馏通道剪裁模型库如下。
 
diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md
index a2fa07cbbd27af81eb1f820956f268e98ec5fd38..b8f782f72b3e4a2bff7cb11aa96ccb6545a457c8 100644
--- a/docs/zh_cn/model_zoo.md
+++ b/docs/zh_cn/model_zoo.md
@@ -199,7 +199,7 @@ PaddleLite版本： v2.3
 | BlazeFace-NAS  |      -      |     8     |   640    |         83.7/80.7/65.8          |      244       |       21.117     |[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) |
 | BlazeFace-NASV2 |    SANAS    |     8     |   640    |         87.0/83.7/68.5          |      389       |       22.558     | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) |
 
-Note: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。BlazeFace-NASV2的详细配置在[这里](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/face_detection/blazeface_nas_v2.yml).
+Note: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。BlazeFace-NASV2的详细配置在[这里](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/face_detection/blazeface_nas_v2.yml)。
 
 ## 3. 图像分割