Merge pull request PaddlePaddle#2 from qingqing01/init_ppdet

Init PaddleDetection.

.gitignore

@@ -0,0 +1,64 @@
+# Virtualenv
+/.venv/
+/venv/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.so
+
+# json file
+*.json
+
+# Distribution / packaging
+/bin/
+/build/
+/develop-eggs/
+/dist/
+/eggs/
+/lib/
+/lib64/
+/output/
+/parts/
+/sdist/
+/var/
+/*.egg-info/
+/.installed.cfg
+/*.egg
+/.eggs
+
+# AUTHORS and ChangeLog will be generated while packaging
+/AUTHORS
+/ChangeLog
+
+# BCloud / BuildSubmitter
+/build_submitter.*
+/logger_client_log
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+.tox/
+.coverage
+.cache
+.pytest_cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+
+# Sphinx documentation
+/docs/_build/
+
+*.json
+
+
+dataset/coco/annotations
+dataset/coco/train2017
+dataset/coco/val2017
+dataset/voc/VOCdevkit

.style.yapf

@@ -0,0 +1,3 @@
+[style]
+based_on_style = pep8
+column_limit = 80

README.md

@@ -0,0 +1,134 @@
+English | [简体中文](README_cn.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.
+
+
+<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)
+- [Guide to traing, evaluate and arguments description](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)
+
+## Model compression
+
+- [ Quantification aware training example](slim/quantization)
+- [ Pruning compression example](slim/prune)
+
+## Depoly
+
+- [Export model for inference depolyment](docs/EXPORT_MODEL.md)
+- [C++ inference depolyment](inference/README.md)
+
+## Benchmark
+
+- [Inference benchmark](docs/BENCHMARK_INFER_cn.md)
+
+
+## Updates
+
+#### 10/2019
+
+- 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.
+
+#### 7/29/2019
+
+- 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
+
+#### 7/3/2019
+
+- 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!!

README_cn.md

@@ -0,0 +1,122 @@
+[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。
+
+<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、评估及参数说明](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。
+
+#### 7/22/2019
+
+- 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
+
+#### 7/3/2019
+
+- 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。