diff --git a/README.md b/README.md
index 6fcc42dddcb48b9c88d74f13a03b24d3c66d9a83..0f0375e7e595485026a3f12f329a68f2a6a4de6d 100644
--- a/README.md
+++ b/README.md
@@ -1,136 +1,127 @@
-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。**
-## 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 [1](#vd) | 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 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ |
+| | ResNet | ResNet-vd [1](#vd) | 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 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ |
-[1] [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
+[1] [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。
diff --git a/README_cn.md b/README_cn.md
deleted file mode 100644
index 37291d5164f01d50b308e2b95ff201bafe75443a..0000000000000000000000000000000000000000
--- a/README_cn.md
+++ /dev/null
@@ -1,125 +0,0 @@
-[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。**
-
-
-
-
-
-
-## 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 [1](#vd) | 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 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ |
-
-[1] [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。
diff --git a/README_en.md b/README_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..e055beadcb1ac5425d3afe32c952a5df6fdf779a
--- /dev/null
+++ b/README_en.md
@@ -0,0 +1,138 @@
+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.**
+
+
+
+
+
+
+## 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 [1](#vd) | 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 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ |
+
+[1] [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!!
diff --git a/contrib/README.md b/contrib/README.md
index 11f93b85b2ddf81176fd7b4c655dbeb28d1e2dc5..52a1179cf018afa5f0564ef71c3a2a5bf6bedb8a 100644
--- a/contrib/README.md
+++ b/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
diff --git a/contrib/README_cn.md b/contrib/README_cn.md
index ca2a0fda33ef9dba514d07cf9c808ec1cd2878e1..d5278bc1ca6055a5532e17c57f5a13e2e16cf3a6 100644
--- a/contrib/README_cn.md
+++ b/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
diff --git a/demo/mask_rcnn_demo.ipynb b/demo/mask_rcnn_demo.ipynb
index 860b185043679e3c7bb28c4fdad505c9f16dda56..f767cf748813134706de393be22bbee79b04fa25 100644
--- a/demo/mask_rcnn_demo.ipynb
+++ b/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"
]
}
],
diff --git a/docs/INSTALL.md b/docs/INSTALL.md
index 7ac6dd8e3e6381972faec3ba86e902988b42e1d4..97b3b14356788be814da3f10aadc9b524ae3b142 100644
--- a/docs/INSTALL.md
+++ b/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
-git clone https://github.com/PaddlePaddle/models
-cd models/PaddleCV/PaddleDetection
+cd
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
```
**Install Python dependencies:**
diff --git a/docs/INSTALL_cn.md b/docs/INSTALL_cn.md
index 5ad6185d5c7a4f6bf558fcd55fc322113d9e6c8c..7ebf95620cb11ecb93e76606549c777dee0210ea 100644
--- a/docs/INSTALL_cn.md
+++ b/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
-git clone https://github.com/PaddlePaddle/models
-cd models/PaddleCV/PaddleDetection
+cd
+git clone https://github.com/PaddlePaddle/PaddleDetection.git
```
**安装Python依赖库:**
diff --git a/docs/MODEL_ZOO.md b/docs/MODEL_ZOO.md
index 7f32742dc8685ca48cc6680111f51647ce49cf92..02e8b7e79d8ddb051f500eee30afe5c975316bd9 100644
--- a/docs/MODEL_ZOO.md
+++ b/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
diff --git a/docs/MODEL_ZOO_cn.md b/docs/MODEL_ZOO_cn.md
index 31794481c08bfea233db20af91813f24ec9b17ce..b889cd9a6b0ab5e24d6aa0c07098af914edd453f 100644
--- a/docs/MODEL_ZOO_cn.md
+++ b/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
diff --git a/slim/distillation/README.md b/slim/distillation/README.md
index e970cc42b54c17a6131c4873662fb2be46767b60..e2666cd01d9ddddb3d74fadab81f8c48f3a0735d 100755
--- a/slim/distillation/README.md
+++ b/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。
diff --git a/slim/prune/README.md b/slim/prune/README.md
index b06fdd2bdd6a3cd75eb00ab7952dfd546b2bfaad..16509624d48fcd42b2d9962bf499daa08f9e6247 100644
--- a/slim/prune/README.md
+++ b/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:
- 检测库的常规训练方法
-- [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)
diff --git a/slim/quantization/README.md b/slim/quantization/README.md
index acb4c9efcbd49bccc4682c7eb7af294885e5d42a..159b7a7f8a1eab2b890fad533bef442efe84ab30 100644
--- a/slim/quantization/README.md
+++ b/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: