move path to PaddleDetection repo in docs (#35)

* move path to PaddleDetection repo in docs

* refine README

README.md

-English | [简体中文](README_cn.md)
+[English](README_en.md) | 简体中文
 
 # PaddleDetection
 
-The goal of PaddleDetection is to provide easy access to a wide range of object
-detection models in both industry and research settings. We design
-PaddleDetection to be not only performant, production-ready but also highly
-flexible, catering to research needs.
+PaddleDetection的目的是为工业界和学术界提供丰富、易用的目标检测模型。不仅性能优越、易于部署，而且能够灵活的满足算法研究的需求。
 
-**Now all models in PaddleDetection require PaddlePaddle version 1.6 or higher, or suitable develop version.**
+**目前检测库下模型均要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。**
 
 <div align="center">
   <img src="demo/output/000000570688.jpg" />
 </div>
 
 
-## Introduction
+## 简介
 
-Features:
+特性：
 
-- Production Ready:
+- 易部署:
 
-  Key operations are implemented in C++ and CUDA, together with PaddlePaddle's
-highly efficient inference engine, enables easy deployment in server environments.
+  PaddleDetection的模型中使用的核心算子均通过C++或CUDA实现，同时基于PaddlePaddle的高性能推理引擎可以方便地部署在多种硬件平台上。
 
-- Highly Flexible:
+- 高灵活度：
 
-  Components are designed to be modular. Model architectures, as well as data
-preprocess pipelines, can be easily customized with simple configuration
-changes.
+  PaddleDetection通过模块化设计来解耦各个组件，基于配置文件可以轻松地搭建各种检测模型。
 
-- Performance Optimized:
+- 高性能：
 
-  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.
+  基于PaddlePaddle框架的高性能内核，在模型训练速度、显存占用上有一定的优势。例如，YOLOv3的训练速度快于其他框架，在Tesla V100 16GB环境下，Mask-RCNN(ResNet50)可以单卡Batch Size可以达到4 (甚至到5)。
 
-Supported Architectures:
+支持的模型结构：
 
 |                    | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet | VGG |
-| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: | :-----: | :--: |
+|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|:---:|
 | Faster R-CNN       | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
 | Faster R-CNN + FPN | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
 | Mask R-CNN         | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
 | Mask R-CNN + FPN   | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
-| Cascade Faster-RCNN |   ✓    |                             ✓ |     ✓      |   ✗   |     ✗     |    ✗    |  ✗   |
-| Cascade Mask-RCNN   |   ✓    |                             ✗ |     ✗      |   ✓   |     ✗     |    ✗    |  ✗   |
-| RetinaNet           |   ✓    |                             ✗ |     ✗      |   ✗   |     ✗     |    ✗    |  ✗   |
+| Cascade Faster-CNN | ✓      |                             ✓ | ✓          | ✗     | ✗         | ✗       | ✗  |
+| Cascade Mask-CNN   | ✓      |                             ✗ | ✗          | ✓     | ✗         | ✗       | ✗   |
+| RetinaNet          | ✓      |                             ✗ | ✓          | ✗     | ✗         | ✗       | ✗   |
 | YOLOv3             | ✓      |                             ✗ | ✗          | ✗     | ✓         | ✓       | ✗   |
 | SSD                | ✗      |                             ✗ | ✗          | ✗     | ✓         | ✗       | ✓   |
 
-<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
+<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型提供了较大的精度提高和较少的性能损失。
 
-Advanced Features:
+扩展特性：
 
-- [x] **Synchronized Batch Norm**: currently used by YOLOv3.
+- [x] **Synchronized Batch Norm**: 目前在YOLOv3中使用。
 - [x] **Group Norm**
 - [x] **Modulated Deformable Convolution**
 - [x] **Deformable PSRoI Pooling**
 
-**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
+**注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。
 
-## Get Started
 
-- [Installation guide](docs/INSTALL.md)
-- [Quick start on small dataset](docs/QUICK_STARTED.md)
-- For detailed training and evaluation workflow, please refer to [GETTING_STARTED](docs/GETTING_STARTED.md)
-- [Guide to preprocess pipeline and custom dataset](docs/DATA.md)
-- [Introduction to the configuration workflow](docs/CONFIG.md)
-- [Examples for detailed configuration explanation](docs/config_example/)
+## 使用教程
+
+- [安装说明](docs/INSTALL_cn.md)
+- [快速开始](docs/QUICK_STARTED_cn.md)
+- [训练、评估流程](docs/GETTING_STARTED_cn.md)
+- [数据预处理及自定义数据集](docs/DATA_cn.md)
+- [配置模块设计和介绍](docs/CONFIG_cn.md)
+- [详细的配置信息和参数说明示例](docs/config_example/)
 - [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
-- [Transfer learning document](docs/TRANSFER_LEARNING.md)
+- [迁移学习教程](docs/TRANSFER_LEARNING_cn.md)
 
-## Model Zoo
+## 模型库
 
-- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md).
-- [Face detection models](configs/face_detection/README.md)
-- [Pretrained models for pedestrian  and vehicle detection](contrib/README.md)
+- [模型库](docs/MODEL_ZOO_cn.md)
+- [人脸检测模型](configs/face_detection/README.md)
+- [行人检测和车辆检测预训练模型](contrib/README_cn.md) 针对不同场景的检测模型
+- [YOLOv3增强模型](docs/YOLOv3_ENHANCEMENT.md) 改进原始YOLOv3，精度达到41.4%，原论文精度为33.0%，同时预测速度也得到提升
+- [Objects365 2019 Challenge夺冠模型](docs/CACascadeRCNN.md) Objects365 Full Track任务中最好的单模型之一,精度达到31.7%
 
-## Model compression
 
-- [Quantization-aware training example](slim/quantization)
-- [Model pruning example](slim/prune)
+## 模型压缩
+- [量化训练压缩示例](slim/quantization)
+- [剪枝压缩示例](slim/prune)
 
-## Deployment
+## 推理部署
 
-- [Export model for inference](docs/EXPORT_MODEL.md)
-- [C++ inference](inference/README.md)
+- [模型导出教程](docs/EXPORT_MODEL.md)
+- [C++推理部署](inference/README.md)
 
 ## Benchmark
 
-- [Inference benchmark](docs/BENCHMARK_INFER_cn.md)
+- [推理Benchmark](docs/BENCHMARK_INFER_cn.md)
 
 
-## Updates
 
-#### 10/2019
+## 版本更新
 
-- Add enhanced YOLOv3 models, box mAP up to 41.4%.
-- Face detection models included: BlazeFace, Faceboxes.
-- Enrich COCO models,  box mAP up to 51.9%.
-- Add CACacascade RCNN, one of the best single model of Objects365 2019 challenge Full Track champion.
-- Add pretrained models for pedestrian and vehicle detection.
-- Support mixed-precision training.
-- Add C++ inference depolyment.
-- Add model compression examples.
+### 10/2019
 
-#### 2/9/2019
+- 增加增强版YOLOv3模型，精度高达41.4%。
+- 增加人脸检测模型BlazeFace、Faceboxes。
+- 丰富基于COCO的模型，精度高达51.9%。
+- 增加Objects365 2019 Challenge上夺冠的最佳单模型之一CACascade-RCNN。
+- 增加行人检测和车辆检测预训练模型。
+- 支持FP16训练。
+- 增加跨平台的C++推理部署方案。
+- 增加模型压缩示例。
 
-- Add retrained models for GroupNorm.
 
-- Add Cascade-Mask-RCNN+FPN.
+### 2/9/2019
+- 增加GroupNorm模型。
+- 增加CascadeRCNN+Mask模型。
 
 #### 5/8/2019
-
-- Add a series of models ralated modulated Deformable Convolution.
+- 增加Modulated Deformable Convolution系列模型。
 
 #### 29/7/2019
 
-- Update Chinese docs for PaddleDetection
-- Fix bug in R-CNN models when train and test at the same time
-- Add ResNext101-vd + Mask R-CNN + FPN models
-- Add YOLOv3 on VOC models
+- 增加检测库中文文档
+- 修复R-CNN系列模型训练同时进行评估的问题
+- 新增ResNext101-vd + Mask R-CNN + FPN模型
+- 新增基于VOC数据集的YOLOv3模型
 
 #### 3/7/2019
 
-- Initial release of PaddleDetection and detection model zoo
-- Models included: Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
-  R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, and SSD.
-
+- 首次发布PaddleDetection检测库和检测模型库
+- 模型包括：Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
+  R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, 和SSD.
 
-## Contributing
+## 如何贡献代码
 
-Contributions are highly welcomed and we would really appreciate your feedback!!
+我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。

README_cn.md

-[English](README.md) | 简体中文
-
-# PaddleDetection
-
-PaddleDetection的目的是为工业界和学术界提供丰富、易用的目标检测模型。不仅性能优越、易于部署，而且能够灵活的满足算法研究的需求。
-
-**目前检测库下模型均要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。**
-
-<div align="center">
-  <img src="demo/output/000000570688.jpg" />
-</div>
-
-
-## 简介
-
-特性：
-
-- 易部署:
-
-  PaddleDetection的模型中使用的核心算子均通过C++或CUDA实现，同时基于PaddlePaddle的高性能推理引擎可以方便地部署在多种硬件平台上。
-
-- 高灵活度：
-
-  PaddleDetection通过模块化设计来解耦各个组件，基于配置文件可以轻松地搭建各种检测模型。
-
-- 高性能：
-
-  基于PaddlePaddle框架的高性能内核，在模型训练速度、显存占用上有一定的优势。例如，YOLOv3的训练速度快于其他框架，在Tesla V100 16GB环境下，Mask-RCNN(ResNet50)可以单卡Batch Size可以达到4 (甚至到5)。
-
-支持的模型结构：
-
-|                    | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet | VGG |
-|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|:---:|
-| Faster R-CNN       | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
-| Faster R-CNN + FPN | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
-| Mask R-CNN         | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
-| Mask R-CNN + FPN   | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
-| Cascade Faster-CNN | ✓      |                             ✓ | ✓          | ✗     | ✗         | ✗       | ✗  |
-| Cascade Mask-CNN   | ✓      |                             ✗ | ✗          | ✓     | ✗         | ✗       | ✗   |
-| RetinaNet          | ✓      |                             ✗ | ✓          | ✗     | ✗         | ✗       | ✗   |
-| YOLOv3             | ✓      |                             ✗ | ✗          | ✗     | ✓         | ✓       | ✗   |
-| SSD                | ✗      |                             ✗ | ✗          | ✗     | ✓         | ✗       | ✓   |
-
-<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型提供了较大的精度提高和较少的性能损失。
-
-扩展特性：
-
-- [x] **Synchronized Batch Norm**: 目前在YOLOv3中使用。
-- [x] **Group Norm**
-- [x] **Modulated Deformable Convolution**
-- [x] **Deformable PSRoI Pooling**
-
-**注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。
-
-
-## 使用教程
-
-- [安装说明](docs/INSTALL_cn.md)
-- [快速开始](docs/QUICK_STARTED_cn.md)
-- [训练、评估流程](docs/GETTING_STARTED_cn.md)
-- [数据预处理及自定义数据集](docs/DATA_cn.md)
-- [配置模块设计和介绍](docs/CONFIG_cn.md)
-- [详细的配置信息和参数说明示例](docs/config_example/)
-- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
-- [迁移学习教程](docs/TRANSFER_LEARNING_cn.md)
-
-## 模型库
-
-- [模型库](docs/MODEL_ZOO_cn.md)
-- [人脸检测模型](configs/face_detection/README.md)
-- [行人检测和车辆检测预训练模型](contrib/README_cn.md)
-
-
-## 模型压缩
-- [量化训练压缩示例](slim/quantization)
-- [剪枝压缩示例](slim/prune)
-
-## 推理部署
-
-- [模型导出教程](docs/EXPORT_MODEL.md)
-- [C++推理部署](inference/README.md)
-
-## Benchmark
-
-- [推理Benchmark](docs/BENCHMARK_INFER_cn.md)
-
-
-
-## 版本更新
-
-### 10/2019
-
-- 增加增强版YOLOv3模型，精度高达41.4%。
-- 增加人脸检测模型BlazeFace、Faceboxes。
-- 丰富基于COCO的模型，精度高达51.9%。
-- 增加Objects365 2019 Challenge上夺冠的最佳单模型之一CACascade-RCNN。
-- 增加行人检测和车辆检测预训练模型。
-- 支持FP16训练。
-- 增加跨平台的C++推理部署方案。
-- 增加模型压缩示例。
-
-
-### 2/9/2019
-- 增加GroupNorm模型。
-- 增加CascadeRCNN+Mask模型。
-
-#### 5/8/2019
-- 增加Modulated Deformable Convolution系列模型。
-
-#### 29/7/2019
-
-- 增加检测库中文文档
-- 修复R-CNN系列模型训练同时进行评估的问题
-- 新增ResNext101-vd + Mask R-CNN + FPN模型
-- 新增基于VOC数据集的YOLOv3模型
-
-#### 3/7/2019
-
-- 首次发布PaddleDetection检测库和检测模型库
-- 模型包括：Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
-  R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, 和SSD.
-
-## 如何贡献代码
-
-我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。

README_en.md

+English | [简体中文](README.md)
+
+# PaddleDetection
+
+The goal of PaddleDetection is to provide easy access to a wide range of object
+detection models in both industry and research settings. We design
+PaddleDetection to be not only performant, production-ready but also highly
+flexible, catering to research needs.
+
+**Now all models in PaddleDetection require PaddlePaddle version 1.6 or higher, or suitable develop version.**
+
+<div align="center">
+  <img src="demo/output/000000570688.jpg" />
+</div>
+
+
+## Introduction
+
+Features:
+
+- 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 | DarkNet | VGG  |
+| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: | :-----: | :--: |
+| Faster R-CNN        |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |    ✗    |  ✗   |
+| Faster R-CNN + FPN  |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |    ✗    |  ✗   |
+| Mask R-CNN          |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |    ✗    |  ✗   |
+| Mask R-CNN + FPN    |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |    ✗    |  ✗   |
+| Cascade Faster-RCNN |   ✓    |                             ✓ |     ✓      |   ✗   |     ✗     |    ✗    |  ✗   |
+| Cascade Mask-RCNN   |   ✓    |                             ✗ |     ✗      |   ✓   |     ✗     |    ✗    |  ✗   |
+| RetinaNet           |   ✓    |                             ✗ |     ✗      |   ✗   |     ✗     |    ✗    |  ✗   |
+| YOLOv3              |   ✓    |                             ✗ |     ✗      |   ✗   |     ✓     |    ✓    |  ✗   |
+| SSD                 |   ✗    |                             ✗ |     ✗      |   ✗   |     ✓     |    ✗    |  ✓   |
+
+<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
+
+Advanced Features:
+
+- [x] **Synchronized Batch Norm**: currently used by YOLOv3.
+- [x] **Group Norm**
+- [x] **Modulated Deformable Convolution**
+- [x] **Deformable PSRoI Pooling**
+
+**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
+
+## Get Started
+
+- [Installation guide](docs/INSTALL.md)
+- [Quick start on small dataset](docs/QUICK_STARTED.md)
+- For detailed training and evaluation workflow, please refer to [GETTING_STARTED](docs/GETTING_STARTED.md)
+- [Guide to preprocess pipeline and custom dataset](docs/DATA.md)
+- [Introduction to the configuration workflow](docs/CONFIG.md)
+- [Examples for detailed configuration explanation](docs/config_example/)
+- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
+- [Transfer learning document](docs/TRANSFER_LEARNING.md)
+
+## Model Zoo
+
+- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md).
+- [Face detection models](configs/face_detection/README.md)
+- [Pretrained models for pedestrian  and vehicle detection](contrib/README.md) Models for object detection in specific scenarios. 
+- [YOLOv3 enhanced model](docs/YOLOv3_ENHANCEMENT.md) Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 41.4% and inference speed is improved as well
+- [Objects365 2019 Challenge champion model](docs/CACascadeRCNN.md) One of the best single models in Objects365 Full Track of which MAP reaches 31.7%.
+
+## Model compression
+
+- [Quantization-aware training example](slim/quantization)
+- [Model pruning example](slim/prune)
+
+## Deployment
+
+- [Export model for inference](docs/EXPORT_MODEL.md)
+- [C++ inference](inference/README.md)
+
+## Benchmark
+
+- [Inference benchmark](docs/BENCHMARK_INFER_cn.md)
+
+
+## Updates
+
+#### 10/2019
+
+- Add enhanced YOLOv3 models, box mAP up to 41.4%.
+- Face detection models included: BlazeFace, Faceboxes.
+- Enrich COCO models,  box mAP up to 51.9%.
+- Add CACacascade RCNN, one of the best single model of Objects365 2019 challenge Full Track champion.
+- Add pretrained models for pedestrian and vehicle detection.
+- Support mixed-precision training.
+- Add C++ inference depolyment.
+- Add model compression examples.
+
+#### 2/9/2019
+
+- Add retrained models for GroupNorm.
+
+- Add Cascade-Mask-RCNN+FPN.
+
+#### 5/8/2019
+
+- Add a series of models ralated modulated Deformable Convolution.
+
+#### 29/7/2019
+
+- Update Chinese docs for PaddleDetection
+- Fix bug in R-CNN models when train and test at the same time
+- Add ResNext101-vd + Mask R-CNN + FPN models
+- Add YOLOv3 on VOC models
+
+#### 3/7/2019
+
+- Initial release of PaddleDetection and detection model zoo
+- Models included: Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
+  R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, and SSD.
+
+
+## Contributing
+
+Contributions are highly welcomed and we would really appreciate your feedback!!

contrib/README.md

@@ -17,7 +17,7 @@ The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53
 
 ### 2. Configuration for training
 
-PaddleDetection provides users with a configuration file [yolov3_darnet.yml](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
+PaddleDetection provides users with a configuration file [yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
 
 * max_iters: 120000
 * num_classes: 6
@@ -67,7 +67,7 @@ The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53
 
 ### 2. Configuration for training
 
-PaddleDetection provides users with a configuration file [yolov3_darnet.yml](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for pedestrian detection:
+PaddleDetection provides users with a configuration file [yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for pedestrian detection:
 
 * max_iters: 200000
 * num_classes: 1

contrib/README_cn.md

@@ -18,7 +18,7 @@ Backbone为Dacknet53的YOLOv3。
 
 ### 2. 训练参数配置
 
-PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darnet.yml](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/configs/yolov3_darknet.yml)，与之相比，在进行车辆检测的模型训练时，我们对以下参数进行了修改：
+PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darnet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml)，与之相比，在进行车辆检测的模型训练时，我们对以下参数进行了修改：
 
 * max_iters: 120000
 * num_classes: 6
@@ -69,7 +69,7 @@ Backbone为Dacknet53的YOLOv3。
 
 ### 2. 训练参数配置
 
-PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darnet.yml](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/configs/yolov3_darknet.yml)，与之相比，在进行行人检测的模型训练时，我们对以下参数进行了修改：
+PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml)，与之相比，在进行行人检测的模型训练时，我们对以下参数进行了修改：
 
 * max_iters: 200000
 * num_classes: 1

demo/mask_rcnn_demo.ipynb

@@ -28,7 +28,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/yang/models/PaddleCV/PaddleDetection\n"
+      "/home/yang/PaddleDetection\n"
      ]
     }
    ],

docs/INSTALL.md

@@ -71,13 +71,11 @@ COCO-API is needed for running. Installation is as follows:
 
 **Clone Paddle models repository:**
 
-You can clone Paddle models and change working directory to PaddleDetection
-with the following commands:
+You can clone PaddleDetection with the following commands:
 
 ```
-cd <path/to/clone/models>
-git clone https://github.com/PaddlePaddle/models
-cd models/PaddleCV/PaddleDetection
+cd <path/to/clone/PaddleDetection>
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
 ```
 
 **Install Python dependencies:**

docs/INSTALL_cn.md

@@ -67,12 +67,11 @@ python -c "import paddle; print(paddle.__version__)"
 
 **克隆Paddle models模型库：**
 
-您可以通过以下命令克隆Paddle models模型库并切换工作目录至PaddleDetection：
+您可以通过以下命令克隆PaddleDetection：
 
 ```
-cd <path/to/clone/models>
-git clone https://github.com/PaddlePaddle/models
-cd models/PaddleCV/PaddleDetection
+cd <path/to/clone/PaddleDetection>
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
 ```
 
 **安装Python依赖库：**

docs/MODEL_ZOO.md

@@ -90,7 +90,7 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
 #### Notes:
 - Deformable ConvNets v2(dcn_v2) reference from [Deformable ConvNets v2](https://arxiv.org/abs/1811.11168).
 - `c3-c5` means adding `dcn` in resnet stage 3 to 5.
-- Detailed configuration file in [configs/dcn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/dcn)
+- Detailed configuration file in [configs/dcn](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn)
 
 ### Group Normalization
 | Backbone             | Type           | Image/gpu | Lr schd | Box AP | Mask AP |                           Download                           |
@@ -100,7 +100,7 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
 
 #### Notes:
 - Group Normalization reference from [Group Normalization](https://arxiv.org/abs/1803.08494).
-- Detailed configuration file in [configs/gn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn)
+- Detailed configuration file in [configs/gn](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gn)
 
 ### YOLO v3
 

docs/MODEL_ZOO_cn.md

@@ -86,7 +86,7 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
 #### 注意事项:
 - Deformable卷积网络v2(dcn_v2)参考自论文[Deformable ConvNets v2](https://arxiv.org/abs/1811.11168).
 - `c3-c5`意思是在resnet模块的3到5阶段增加`dcn`.
-- 详细的配置文件在[configs/dcn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/dcn)
+- 详细的配置文件在[configs/dcn](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn)
 
 ### Group Normalization
 | 骨架网络             | 网络类型           | 每张GPU图片个数 | 学习率策略 | Box AP | Mask AP |                           下载                           |
@@ -96,7 +96,7 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
 
 #### 注意事项:
 - Group Normalization参考论文[Group Normalization](https://arxiv.org/abs/1803.08494).
-- 详细的配置文件在[configs/gn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn)
+- 详细的配置文件在[configs/gn](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gn)
 
 ### YOLO v3
 

slim/distillation/README.md

@@ -7,7 +7,7 @@
 该示例使用PaddleSlim提供的[蒸馏策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#3-蒸馏)对检测库中的模型进行蒸馏训练。
 在阅读该示例前，建议您先了解以下内容：
 
-- [检测库的常规训练方法](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection)
+- [检测库的常规训练方法](https://github.com/PaddlePaddle/PaddleDetection)
 - [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
 
 
@@ -61,7 +61,7 @@ strategies:
 
 ## 训练
 
-根据[PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/tools/train.py)编写压缩脚本compress.py。
+根据[PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/PaddleDetection/tree/master/tools/train.py)编写压缩脚本compress.py。
 在该脚本中定义了Compressor对象，用于执行压缩任务。
 
 

slim/prune/README.md

@@ -8,7 +8,7 @@
 在阅读该示例前，建议您先了解以下内容：
 
 - <a href="../../README_cn.md">检测库的常规训练方法</a>
-- [检测模型数据准备](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/INSTALL_cn.md#%E6%95%B0%E6%8D%AE%E9%9B%86)
+- [检测模型数据准备](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/INSTALL_cn.md#%E6%95%B0%E6%8D%AE%E9%9B%86)
 - [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
 
 

slim/quantization/README.md

@@ -7,7 +7,7 @@
 该示例使用PaddleSlim提供的[量化压缩策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#1-quantization-aware-training%E9%87%8F%E5%8C%96%E4%BB%8B%E7%BB%8D)对分类模型进行压缩。
 在阅读该示例前，建议您先了解以下内容：
 
-- [检测模型的常规训练方法](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection)
+- [检测模型的常规训练方法](https://github.com/PaddlePaddle/PaddleDetection)
 - [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
 
 
@@ -29,7 +29,7 @@
 根据运行结果可看到Variable的名字为：`multiclass_nms_0.tmp_0`。
 ## 训练
 
-根据 [PaddleCV/PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/tools/train.py) 编写压缩脚本compress.py。
+根据 [tools/train.py](https://github.com/PaddlePaddle/PaddleDetection/tree/master/tools/train.py) 编写压缩脚本compress.py。
 在该脚本中定义了Compressor对象，用于执行压缩任务。
 
 通过`python compress.py --help`查看可配置参数，简述如下：