diff --git a/README_cn.md b/README_cn.md
index c4c77a5c67eb830a9437c9de258d8bffd378113b..d09eddbae03bdf79e795ba3f67af0e0d46447a99 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -18,7 +18,7 @@ PaddleDetection模块化地实现了多种主流目标检测算法，提供了
 </div>
 
 ### 产品动态
-- 2021.04.14: 发布release/2.0版本，PaddleDetection全面支持动态图，覆盖静态图模型算法，全面升级模型效果，同时发布PPYOLOv2模型，新增旋转框检测S2ANet模型，详情参考[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.0)
+- 2021.04.14: 发布release/2.0版本，PaddleDetection全面支持动态图，覆盖静态图模型算法，全面升级模型效果，同时发布PP-YOLO v2模型，新增旋转框检测S2ANet模型，详情参考[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.0)
 - 2021.02.07: 发布release/2.0-rc版本，PaddleDetection动态图试用版本，详情参考[PaddleDetection动态图](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.0-rc)。
 
 ### 特性
@@ -180,8 +180,8 @@ PaddleDetection模块化地实现了多种主流目标检测算法，提供了
 
 - `CBResNet`为`Cascade-Faster-RCNN-CBResNet200vd-FPN`模型，COCO数据集mAP高达53.3%
 - `Cascade-Faster-RCNN`为`Cascade-Faster-RCNN-ResNet50vd-DCN`，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS
-- `PPYOLO`在COCO数据集精度45.9%，Tesla V100预测速度72.9FPS，精度速度均优于[YOLOv4](https://arxiv.org/abs/2004.10934)
-- `PPYOLOv2`是对`PPYOLO`模型的进一步优化啊，在COCO数据集精度49.5%，Tesla V100预测速度60FPS
+- `PP-YOLO`在COCO数据集精度45.9%，Tesla V100预测速度72.9FPS，精度速度均优于[YOLOv4](https://arxiv.org/abs/2004.10934)
+- `PP-YOLO v2`是对`PP-YOLO`模型的进一步优化，在COCO数据集精度49.5%，Tesla V100预测速度60FPS
 - 图中模型均可在[模型库](#模型库)中获取
 
 ## 文档教程
@@ -195,17 +195,18 @@ PaddleDetection模块化地实现了多种主流目标检测算法，提供了
 
 ### 进阶教程
 
-- [参数配置]
+- 参数配置
     - [RCNN参数说明](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation.md)
-    - [PPYOLO参数说明](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation.md)
-- [模型压缩(基于[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))](configs/slim)
+    - [PP-YOLO参数说明](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation.md)
+- 模型压缩(基于[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
+    - [剪裁/量化/蒸馏教程](configs/slim)
 - [推理部署](deploy/README.md)
     - [模型导出教程](deploy/EXPORT_MODEL.md)
     - [Python端推理部署](deploy/python)
     - [C++端推理部署](deploy/cpp)
     - [服务端部署](deploy/serving)
     - [推理benchmark](deploy/BENCHMARK_INFER.md)
-- [进阶开发]
+- 进阶开发
     - [数据处理模块](docs/advanced_tutorials/READER.md)
     - [新增检测模型](docs/advanced_tutorials/MODEL_TECHNICAL.md)
 
@@ -215,7 +216,7 @@ PaddleDetection模块化地实现了多种主流目标检测算法，提供了
 - 通用目标检测:
     - [模型库](docs/MODEL_ZOO_cn.md)
     - [移动端模型](static/configs/mobile/README.md)
-    - [PPYOLO模型](configs/ppyolo/README_cn.md)
+    - [PP-YOLO模型](configs/ppyolo/README_cn.md)
     - [676类目标检测](static/docs/featured_model/LARGE_SCALE_DET_MODEL.md)
     - [两阶段实用模型PSS-Det](configs/rcnn_enhance/README.md)
     - [TTFNet](configs/ttfnet/README.md)
@@ -245,7 +246,7 @@ PaddleDetection模块化地实现了多种主流目标检测算法，提供了
 
 ## 版本更新
 
-v2.0版本已经在`04/2021`发布，全面支持动态图版本，新增支持BlazeFace, PSSDet等系列模型和大量骨干网络，发布PPYOLOv2, PPYOLO-tiny和旋转框检测S2ANet模型。支持模型蒸馏、VisualDL，新增动态图预测部署benchmark，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
+v2.0版本已经在`04/2021`发布，全面支持动态图版本，新增支持BlazeFace, PSSDet等系列模型和大量骨干网络，发布PP-YOLO v2, PP-YOLO tiny和旋转框检测S2ANet模型。支持模型蒸馏、VisualDL，新增动态图预测部署benchmark，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
 
 
 ## 许可证书
diff --git a/README_en.md b/README_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..72fd84c3709e471c75124893bc088875da6030da
--- /dev/null
+++ b/README_en.md
@@ -0,0 +1,275 @@
+English | [简体中文](README_cn.md)
+
+### PaddleDetection 2.0 is ready! Dygraph mode is set by default and static graph code base is [here](static)
+
+# Introduction
+
+PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of constructing, training, optimizing and deploying detection models in a faster and better way.
+
+PaddleDetection implements varied mainstream object detection algorithms in modular design, and provides wealthy data augmentation methods, network components(such as backbones), loss functions, etc., and integrates abilities of model compression and cross-platform high-performance deployment.
+
+After a long time of industry practice polishing, PaddleDetection has had smooth and excellent user experience, it has been widely used by developers in more than ten industries such as industrial quality inspection, remote sensing image object detection, automatic inspection, new retail, Internet, and scientific research.
+
+<div align="center">
+  <img src="static/docs/images/football.gif" width='800'/>
+</div>
+
+### Product news
+
+- 2021.04.14: Release `release/2.0` version. Dygraph mode in PaddleDetection is fully supported. Cover all the algorithm of static graph and update the performance of mainstream detection models. Release `PP-YOLO v2` and `S2ANet` which is aimed at rotation object detection.Please refer to [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.0) for details.
+- 2020.02.07: Release `release/2.0-rc` version, Please refer to [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.0-rc) for details.
+
+
+### Features
+
+- **Rich Models**
+PaddleDetection provides rich of models, including **100+ pre-trained models** such as **object detection**, **instance segmentation**, **face detection** etc. It covers a variety of **global competition champion** schemes.
+
+- **Highly Flexible:**
+Components are designed to be modular. Model architectures, as well as data preprocess pipelines and optimization strategies, can be easily customized with simple configuration changes.
+
+- **Production Ready:**
+From data augmentation, constructing models, training, compression, depolyment, get through end to end, and complete support for multi-architecture, multi-device deployment for **cloud and edge device**.
+
+- **High Performance:**
+Based on the high performance core of PaddlePaddle, advantages of training speed and memory occupation are obvious. FP16 training and multi-machine training are supported as well.
+
+#### Overview of Kit Structures
+
+<table>
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>Architectures</b>
+      </td>
+      <td>
+        <b>Backbones</b>
+      </td>
+      <td>
+        <b>Components</b>
+      </td>
+      <td>
+        <b>Data Augmentation</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+        <ul><li><b>Two-Stage Detection</b></li>
+          <ul>
+            <li>Faster RCNN</li>
+            <li>FPN</li>
+            <li>Cascade-RCNN</li>
+            <li>Libra RCNN</li>
+            <li>Hybrid Task RCNN</li>
+            <li>PSS-Det RCNN</li>
+          </ul>
+        </ul>
+        <ul><li><b>One-Stage Detection</b></li>
+          <ul>
+            <li>RetinaNet</li>
+            <li>YOLOv3</li>
+            <li>YOLOv4</li>  
+            <li>PP-YOLO</li>
+            <li>SSD</li>
+          </ul>
+        </ul>
+        <ul><li><b>Anchor Free</b></li>
+          <ul>
+            <li>CornerNet-Squeeze</li>
+            <li>FCOS</li>  
+            <li>TTFNet</li>
+          </ul>
+        </ul>
+        <ul>
+          <li><b>Instance Segmentation</b></li>
+            <ul>
+             <li>Mask RCNN</li>
+             <li>SOLOv2</li>
+            </ul>
+        </ul>
+        <ul>
+          <li><b>Face-Detction</b></li>
+            <ul>
+             <li>FaceBoxes</li>
+             <li>BlazeFace</li>
+             <li>BlazeFace-NAS</li>
+            </ul>
+        </ul>
+      </td>
+      <td>
+        <ul>
+          <li>ResNet(&vd)</li>
+          <li>ResNeXt(&vd)</li>
+          <li>SENet</li>
+          <li>Res2Net</li>
+          <li>HRNet</li>
+          <li>Hourglass</li>
+          <li>CBNet</li>
+          <li>GCNet</li>
+          <li>DarkNet</li>
+          <li>CSPDarkNet</li>
+          <li>VGG</li>
+          <li>MobileNetv1/v3</li>  
+          <li>GhostNet</li>
+          <li>Efficientnet</li>  
+        </ul>
+      </td>
+      <td>
+        <ul><li><b>Common</b></li>
+          <ul>
+            <li>Sync-BN</li>
+            <li>Group Norm</li>
+            <li>DCNv2</li>
+            <li>Non-local</li>
+          </ul>  
+        </ul>
+        <ul><li><b>FPN</b></li>
+          <ul>
+            <li>BiFPN</li>
+            <li>BFP</li>  
+            <li>HRFPN</li>
+            <li>ACFPN</li>
+          </ul>  
+        </ul>  
+        <ul><li><b>Loss</b></li>
+          <ul>
+            <li>Smooth-L1</li>
+            <li>GIoU/DIoU/CIoU</li>  
+            <li>IoUAware</li>
+          </ul>  
+        </ul>  
+        <ul><li><b>Post-processing</b></li>
+          <ul>
+            <li>SoftNMS</li>
+            <li>MatrixNMS</li>  
+          </ul>  
+        </ul>
+        <ul><li><b>Speed</b></li>
+          <ul>
+            <li>FP16 training</li>
+            <li>Multi-machine training </li>  
+          </ul>  
+        </ul>  
+      </td>
+      <td>
+        <ul>
+          <li>Resize</li>  
+          <li>Flipping</li>  
+          <li>Expand</li>
+          <li>Crop</li>
+          <li>Color Distort</li>  
+          <li>Random Erasing</li>  
+          <li>Mixup </li>
+          <li>Cutmix </li>
+          <li>Grid Mask</li>
+          <li>Auto Augment</li>  
+        </ul>  
+      </td>  
+    </tr>
+
+
+</td>
+    </tr>
+  </tbody>
+</table>
+
+#### Overview of Model Performance
+The relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
+
+<div align="center">
+  <img src="docs/images/fps_map.png" />
+</div>
+
+**NOTE:**
+
+- `CBResNet stands` for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3%
+
+- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models
+
+- `PP-YOLO` achieves mAP of 45.9% on COCO and 72.9FPS on Tesla V100. Both precision and speed surpass [YOLOv4](https://arxiv.org/abs/2004.10934)
+
+- `PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 60FPS on Tesla V100
+
+- All these models can be get in [Model Zoo](#ModelZoo)
+
+
+## Tutorials
+
+### Get Started
+
+- [Installation guide](docs/tutorials/INSTALL_en.md)
+- [Quick start on small dataset](docs/tutorials/QUICK_STARTED_en.md)
+- [Prepare dataset](docs/tutorials/PrepareDataSet.md)
+- [Train/Evaluation/Inference/Deploy](docs/tutorials/GETTING_STARTED_en.md)
+
+
+### Advanced Tutorials
+
+- Parameter configuration
+  - [Parameter configuration for RCNN model](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation.md)
+  - [Parameter configuration for PP-YOLO model](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation.md)
+
+- Model Compression(Based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
+  - [Prune/Quant/Distill](configs/slim)
+
+- Inference and deployment
+  - [Export model for inference](deploy/EXPORT_MODEL.md)
+  - [Python inference](deploy/python)
+  - [C++ inference](deploy/cpp)
+  - [Serving](deploy/serving)
+  - [Inference benchmark](deploy/BENCHMARK_INFER.md)
+
+- Advanced development
+  - [New data augmentations](docs/advanced_tutorials/READER.md)
+  - [New detection algorithms](docs/advanced_tutorials/MODEL_TECHNICAL.md)
+
+
+## Model Zoo
+
+- Universal object detection
+  - [Model library and baselines](docs/MODEL_ZOO_cn.md)
+  - [Mobile models](static/configs/mobile/README.md)
+  - [PP-YOLO](configs/ppyolo/README.md)
+  - [676 classes of object detection](static/docs/featured_model/LARGE_SCALE_DET_MODEL.md)
+  - [Two-stage practical PSS-Det](configs/rcnn_enhance/README.md)
+  - [TTFNet](configs/ttfnet/README.md)
+- Universal instance segmentation
+  - [SOLOv2](configs/solov2/README.md)
+- Rotation object detection
+  - [S2ANet](configs/dota/README.md)
+- Vertical field
+  - [Face detection](configs/face_detection/README.md)
+  - [Pedestrian detection](configs/pedestrian/README.md)
+  - [Vehicle detection](configs/vehicle/README.md)
+- Competition Plan
+  - [Objects365 2019 Challenge champion model](static/docs/featured_model/champion_model/CACascadeRCNN.md)
+  - [Best single model of Open Images 2019-Object Detction](static/docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
+
+## Applications
+
+- [Christmas portrait automatic generation tool](static/application/christmas)
+
+## Updates
+
+v2.0 was released at `04/2021`, fully support dygraph version, which add BlazeFace, PSS-Det and plenty backbones, release `PP-YOLOv2`, `PP-YOLO tiny` and `S2ANet`, support model distillation and VisualDL, add inference benchmark, etc. Please refer to [change log](docs/CHANGELOG.md) for details.
+
+
+## License
+
+PaddleDetection is released under the [Apache 2.0 license](LICENSE).
+
+
+## Contributing
+
+Contributions are highly welcomed and we would really appreciate your feedback!!
+
+## Citation
+
+```
+@misc{ppdet2019,
+title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
+author={PaddlePaddle Authors},
+howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
+year={2019}
+}
+```