Change README to cn (#1430)

test=document_fix

README.md

-English | [简体中文](README_cn.md)
-
-Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
-
-# PaddleDetection
-
-PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which
-aims to help developers in the whole development of training models, optimizing performance and
-inference speed, and deploying models. PaddleDetection provides varied object detection architectures
-in modular design, and wealthy data augmentation methods, network components, loss functions, etc.
-PaddleDetection supported practical projects such as industrial quality inspection, remote sensing
-image object detection, and automatic inspection with its practical features such as model compression
-and multi-platform deployment.
-
-[PP-YOLO](https://arxiv.org/abs/2007.12099), which is faster and has higer performance than YOLOv4,
-has been released, it reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single
-Tesla V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
-
-**Now all models in PaddleDetection require PaddlePaddle version 1.8 or higher, or suitable develop version.**
-
-<div align="center">
-  <img src="docs/images/000000570688.jpg" />
-</div>
-
-
-## Introduction
-
-Features:
-
-- Rich models:
-
-  PaddleDetection provides rich of models, including 100+ pre-trained models
-such as object detection, instance segmentation, face detection etc. It covers
-the champion models, the practical detection models for cloud and edge device.
-
-- Production Ready:
-
-  Key operations are implemented in C++ and CUDA, together with PaddlePaddle's
-highly efficient inference engine, enables easy deployment in server environments.
-
-- Highly Flexible:
-
-  Components are designed to be modular. Model architectures, as well as data
-preprocess pipelines, can be easily customized with simple configuration
-changes.
-
-- Performance Optimized:
-
-  With the help of the underlying PaddlePaddle framework, faster training and
-reduced GPU memory footprint is achieved. Notably, YOLOv3 training is
-much faster compared to other frameworks. Another example is Mask-RCNN
-(ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during
-multi-GPU training.
-
-Supported Architectures:
-
-|                     | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet |  HRNet | Res2Net |
-| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: |:------:|:-----:  |
-| Faster R-CNN        |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |   ✗    |  ✗      |
-| Faster R-CNN + FPN  |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |   ✓    |  ✓      |
-| Mask R-CNN          |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |   ✗    |  ✗      |
-| Mask R-CNN + FPN    |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |   ✗    |  ✓      |
-| Cascade Faster-RCNN |   ✓    |                             ✓ |     ✓      |   ✗   |     ✗     |   ✗    |  ✗      |
-| Cascade Mask-RCNN   |   ✓    |                             ✗ |     ✗      |   ✓   |     ✗     |   ✗    |  ✗      |
-| Libra R-CNN         |   ✗    |                             ✓ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
-| RetinaNet           |   ✓    |                             ✗ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
-| YOLOv3              |   ✓    |                             ✓ |     ✗      |   ✗   |     ✓     |   ✗    |  ✗      |
-| SSD                 |   ✗    |                             ✗ |     ✗      |   ✗   |     ✓     |   ✗    |  ✗      |
-| BlazeFace           |   ✗    |                             ✗ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
-| Faceboxes           |   ✗    |                             ✗ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
-
-<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
-
-**NOTE:** ✓ for config file and pretrain model provided in [Model Zoo](docs/MODEL_ZOO.md), ✗ for not provided but is supported generally.
-
-More models:
-
-- EfficientDet
-- FCOS
-- CornerNet-Squeeze
-- YOLOv4
-- PP-YOLO
-
-More Backbones:
-
-- DarkNet
-- VGG
-- GCNet
-- CBNet
-
-Advanced Features:
-
-- [x] **Synchronized Batch Norm**
-- [x] **Group Norm**
-- [x] **Modulated Deformable Convolution**
-- [x] **Deformable PSRoI Pooling**
-- [x] **Non-local and GCNet**
-
-**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
-
-The following is 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/map_fps.png" width=800 />
-</div>
-
-**NOTE:**
-- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% in PaddleDetection models
-- `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%
-- The enhanced `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is nearly 70% faster than the darknet framework
-- All these models can be get in [Model Zoo](#Model-Zoo)
-
-The following is the relationship between COCO mAP and FPS on Tesla V100 of SOTA object detecters and PP-YOLO, which is faster and has better performance than YOLOv4, and reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
-
-<div align="center">
-  <img src="docs/images/ppyolo_map_fps.png" width=600 />
-</div>
-
-## Tutorials
-
-
-### Get Started
-
-- [Installation guide](docs/tutorials/INSTALL.md)
-- [Quick start on small dataset](docs/tutorials/QUICK_STARTED.md)
-- [Train/Evaluation/Inference](docs/tutorials/GETTING_STARTED.md)
-- [How to train a custom dataset](docs/tutorials/Custom_DataSet.md)
-- [FAQ](docs/FAQ.md)
-
-### Advanced Tutorial
-
-- [Guide to preprocess pipeline and dataset definition](docs/advanced_tutorials/READER.md)
-- [Models technical](docs/advanced_tutorials/MODEL_TECHNICAL.md)
-- [Transfer learning document](docs/advanced_tutorials/TRANSFER_LEARNING.md)
-- [Parameter configuration](docs/advanced_tutorials/config_doc):
-  - [Introduction to the configuration workflow](docs/advanced_tutorials/config_doc/CONFIG.md)
-  - [Parameter configuration for RCNN model](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
-- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
-- [Model compression](slim)
-    - [Model compression benchmark](slim)
-    - [Quantization](slim/quantization)
-    - [Model pruning](slim/prune)
-    - [Model distillation](slim/distillation)
-    - [Neural Architecture Search](slim/nas)
-- [Deployment](deploy)
-    - [Export model for inference](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
-    - [Python inference](deploy/python)
-    - [C++ inference](deploy/cpp)
-    - [Inference benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md)
-
-## Model Zoo
-
-- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md).
-- [Mobile models](configs/mobile/README.md)
-- [Anchor free models](configs/anchor_free/README.md)
-- [Face detection models](docs/featured_model/FACE_DETECTION_en.md)
-- [Pretrained models for pedestrian detection](docs/featured_model/CONTRIB.md)
-- [Pretrained models for vehicle detection](docs/featured_model/CONTRIB.md)
-- [YOLOv3 enhanced model](docs/featured_model/YOLOv3_ENHANCEMENT.md): Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 43.6%, and inference speed is improved as well
-- [PP-YOLO](configs/ppyolo/README.md): PP-YOLO reeached mAP as 45.3% on COCO dataset，and 72.9 FPS on single Tesla V100
-- [Objects365 2019 Challenge champion model](docs/featured_model/champion_model/CACascadeRCNN.md)
-- [Best single model of Open Images 2019-Object Detction](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
-- [Practical Server-side detection method](configs/rcnn_enhance/README_en.md): Inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%.
-- [Large-scale practical object detection models](docs/featured_model/LARGE_SCALE_DET_MODEL_en.md): Large-scale practical server-side detection pretrained models with 676 categories are provided for most application scenarios, which can be used not only for direct inference but also finetuning on other datasets.
-
-
-## License
-PaddleDetection is released under the [Apache 2.0 license](LICENSE).
-
-## Updates
-v0.4.0 was released at `05/2020`, add PP-YOLO, TTFNet, HTC, ACFPN, etc. And add BlaceFace face landmark detection model, add a series of optimized SSDLite models on mobile side, add data augmentations GridMask and RandomErasing, add Matrix NMS and EMA training, and improved ease of use, fix many known bugs, etc.
-Please refer to [版本更新文档](docs/CHANGELOG.md) for details.
-
-## Contributing
-
-Contributions are highly welcomed and we would really appreciate your feedback!!

README.md

+README_cn.md
\ No newline at end of file

README_cn.md

-简体中文 | [English](README.md)
+简体中文 | [English](README_en.md)
 
 文档：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
 

README_en.md

+English | [简体中文](README_cn.md)
+
+Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
+
+# PaddleDetection
+
+PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which
+aims to help developers in the whole development of training models, optimizing performance and
+inference speed, and deploying models. PaddleDetection provides varied object detection architectures
+in modular design, and wealthy data augmentation methods, network components, loss functions, etc.
+PaddleDetection supported practical projects such as industrial quality inspection, remote sensing
+image object detection, and automatic inspection with its practical features such as model compression
+and multi-platform deployment.
+
+[PP-YOLO](https://arxiv.org/abs/2007.12099), which is faster and has higer performance than YOLOv4,
+has been released, it reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single
+Tesla V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
+
+**Now all models in PaddleDetection require PaddlePaddle version 1.8 or higher, or suitable develop version.**
+
+<div align="center">
+  <img src="docs/images/000000570688.jpg" />
+</div>
+
+
+## Introduction
+
+Features:
+
+- Rich models:
+
+  PaddleDetection provides rich of models, including 100+ pre-trained models
+such as object detection, instance segmentation, face detection etc. It covers
+the champion models, the practical detection models for cloud and edge device.
+
+- Production Ready:
+
+  Key operations are implemented in C++ and CUDA, together with PaddlePaddle's
+highly efficient inference engine, enables easy deployment in server environments.
+
+- Highly Flexible:
+
+  Components are designed to be modular. Model architectures, as well as data
+preprocess pipelines, can be easily customized with simple configuration
+changes.
+
+- Performance Optimized:
+
+  With the help of the underlying PaddlePaddle framework, faster training and
+reduced GPU memory footprint is achieved. Notably, YOLOv3 training is
+much faster compared to other frameworks. Another example is Mask-RCNN
+(ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during
+multi-GPU training.
+
+Supported Architectures:
+
+|                     | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet |  HRNet | Res2Net |
+| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: |:------:|:-----:  |
+| Faster R-CNN        |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |   ✗    |  ✗      |
+| Faster R-CNN + FPN  |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |   ✓    |  ✓      |
+| Mask R-CNN          |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |   ✗    |  ✗      |
+| Mask R-CNN + FPN    |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |   ✗    |  ✓      |
+| Cascade Faster-RCNN |   ✓    |                             ✓ |     ✓      |   ✗   |     ✗     |   ✗    |  ✗      |
+| Cascade Mask-RCNN   |   ✓    |                             ✗ |     ✗      |   ✓   |     ✗     |   ✗    |  ✗      |
+| Libra R-CNN         |   ✗    |                             ✓ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
+| RetinaNet           |   ✓    |                             ✗ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
+| YOLOv3              |   ✓    |                             ✓ |     ✗      |   ✗   |     ✓     |   ✗    |  ✗      |
+| SSD                 |   ✗    |                             ✗ |     ✗      |   ✗   |     ✓     |   ✗    |  ✗      |
+| BlazeFace           |   ✗    |                             ✗ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
+| Faceboxes           |   ✗    |                             ✗ |     ✗      |   ✗   |     ✗     |   ✗    |  ✗      |
+
+<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
+
+**NOTE:** ✓ for config file and pretrain model provided in [Model Zoo](docs/MODEL_ZOO.md), ✗ for not provided but is supported generally.
+
+More models:
+
+- EfficientDet
+- FCOS
+- CornerNet-Squeeze
+- YOLOv4
+- PP-YOLO
+
+More Backbones:
+
+- DarkNet
+- VGG
+- GCNet
+- CBNet
+
+Advanced Features:
+
+- [x] **Synchronized Batch Norm**
+- [x] **Group Norm**
+- [x] **Modulated Deformable Convolution**
+- [x] **Deformable PSRoI Pooling**
+- [x] **Non-local and GCNet**
+
+**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
+
+The following is 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/map_fps.png" width=800 />
+</div>
+
+**NOTE:**
+- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% in PaddleDetection models
+- `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%
+- The enhanced `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is nearly 70% faster than the darknet framework
+- All these models can be get in [Model Zoo](#Model-Zoo)
+
+The following is the relationship between COCO mAP and FPS on Tesla V100 of SOTA object detecters and PP-YOLO, which is faster and has better performance than YOLOv4, and reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
+
+<div align="center">
+  <img src="docs/images/ppyolo_map_fps.png" width=600 />
+</div>
+
+## Tutorials
+
+
+### Get Started
+
+- [Installation guide](docs/tutorials/INSTALL.md)
+- [Quick start on small dataset](docs/tutorials/QUICK_STARTED.md)
+- [Train/Evaluation/Inference](docs/tutorials/GETTING_STARTED.md)
+- [How to train a custom dataset](docs/tutorials/Custom_DataSet.md)
+- [FAQ](docs/FAQ.md)
+
+### Advanced Tutorial
+
+- [Guide to preprocess pipeline and dataset definition](docs/advanced_tutorials/READER.md)
+- [Models technical](docs/advanced_tutorials/MODEL_TECHNICAL.md)
+- [Transfer learning document](docs/advanced_tutorials/TRANSFER_LEARNING.md)
+- [Parameter configuration](docs/advanced_tutorials/config_doc):
+  - [Introduction to the configuration workflow](docs/advanced_tutorials/config_doc/CONFIG.md)
+  - [Parameter configuration for RCNN model](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
+- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
+- [Model compression](slim)
+    - [Model compression benchmark](slim)
+    - [Quantization](slim/quantization)
+    - [Model pruning](slim/prune)
+    - [Model distillation](slim/distillation)
+    - [Neural Architecture Search](slim/nas)
+- [Deployment](deploy)
+    - [Export model for inference](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
+    - [Python inference](deploy/python)
+    - [C++ inference](deploy/cpp)
+    - [Inference benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md)
+
+## Model Zoo
+
+- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md).
+- [Mobile models](configs/mobile/README.md)
+- [Anchor free models](configs/anchor_free/README.md)
+- [Face detection models](docs/featured_model/FACE_DETECTION_en.md)
+- [Pretrained models for pedestrian detection](docs/featured_model/CONTRIB.md)
+- [Pretrained models for vehicle detection](docs/featured_model/CONTRIB.md)
+- [YOLOv3 enhanced model](docs/featured_model/YOLOv3_ENHANCEMENT.md): Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 43.6%, and inference speed is improved as well
+- [PP-YOLO](configs/ppyolo/README.md): PP-YOLO reeached mAP as 45.3% on COCO dataset，and 72.9 FPS on single Tesla V100
+- [Objects365 2019 Challenge champion model](docs/featured_model/champion_model/CACascadeRCNN.md)
+- [Best single model of Open Images 2019-Object Detction](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
+- [Practical Server-side detection method](configs/rcnn_enhance/README_en.md): Inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%.
+- [Large-scale practical object detection models](docs/featured_model/LARGE_SCALE_DET_MODEL_en.md): Large-scale practical server-side detection pretrained models with 676 categories are provided for most application scenarios, which can be used not only for direct inference but also finetuning on other datasets.
+
+
+## License
+PaddleDetection is released under the [Apache 2.0 license](LICENSE).
+
+## Updates
+v0.4.0 was released at `05/2020`, add PP-YOLO, TTFNet, HTC, ACFPN, etc. And add BlaceFace face landmark detection model, add a series of optimized SSDLite models on mobile side, add data augmentations GridMask and RandomErasing, add Matrix NMS and EMA training, and improved ease of use, fix many known bugs, etc.
+Please refer to [版本更新文档](docs/CHANGELOG.md) for details.
+
+## Contributing
+
+Contributions are highly welcomed and we would really appreciate your feedback!!