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# PaddleDetection
**注意：** PaddleDetection动态图版本为试用版本，模型广度、模型性能、文档、易用性和兼容性持续优化中，性能数据待发布。
# 简介
PaddleDetection飞桨目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。
PaddleDetection模块化地实现了多种主流目标检测算法，提供了丰富的数据增强策略、网络模块组件（如骨干网络）、损失函数等，并集成了模型压缩和跨平台高性能部署能力。
经过长时间产业实践打磨，PaddleDetection已拥有顺畅、卓越的使用体验，被工业质检、遥感图像检测、无人巡检、新零售、互联网、科研等十多个行业的开发者广泛应用。
### 套件结构概览
<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>PSS-Det RCNN</li>
</ul>
</ul>
<ul><li><b>One-Stage Detection</b></li>
<ul>
<li>YOLOv3</li>
<li>PP-YOLO</li>
<li>SSD</li>
</ul>
</ul>
<ul><li><b>Anchor Free</b></li>
<ul>
<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>BlazeFace</li>
</ul>
</ul>
</td>
<td>
<ul>
<li>ResNet(&vd)</li>
<li>HRNet</li>
<li>DarkNet</li>
<li>VGG</li>
<li>MobileNetv1/v3</li>
</ul>
</td>
<td>
<ul><li><b>Common</b></li>
<ul>
<li>Sync-BN</li>
<li>DCNv2</li>
</ul>
</ul>
<ul><li><b>Loss</b></li>
<ul>
<li>Smooth-L1 Loss</li>
<li>IoU Loss</li>
<li>IoU Aware Loss</li>
</ul>
</ul>
<ul><li><b>Post-processing</b></li>
<ul>
<li>SoftNMS</li>
<li>MatrixNMS</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>
### 扩展特性
- [√] **Synchronized Batch Norm**
- [√] **Modulated Deformable Convolution**
- [x] **Group Norm**
- [x] **Deformable PSRoI Pooling**
## 文档教程
### 入门教程
- [安装说明](docs/tutorials/INSTALL_cn.md)
- [快速开始](docs/tutorials/QUICK_STARTED_cn.md)
- [如何准备数据](docs/tutorials/PrepareDataSet.md)
- [训练/评估/预测流程](docs/tutorials/GETTING_STARTED_cn.md)
### 进阶教程
- [模型压缩](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]
- [数据处理模块](docs/advanced_tutorials/READER.md)
- [新增检测模型](docs/advanced_tutorials/MODEL_TECHNICAL.md)
## 模型库
- 通用目标检测:
- [模型库](docs/MODEL_ZOO_cn.md)
- 垂类领域:
- [行人检测](configs/pedestrian/README.md)
- [车辆检测](configs/vehicle/README.md)
## 许可证书
本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
## 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。
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简体中文 | [English](README_en.md)
# PaddleDetection
### PaddleDetection 2.0全面升级！目前默认使用动态图版本，静态图版本位于[static](./static)中
# 简介
PaddleDetection飞桨目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。
PaddleDetection模块化地实现了多种主流目标检测算法，提供了丰富的数据增强策略、网络模块组件（如骨干网络）、损失函数等，并集成了模型压缩和跨平台高性能部署能力。
经过长时间产业实践打磨，PaddleDetection已拥有顺畅、卓越的使用体验，被工业质检、遥感图像检测、无人巡检、新零售、互联网、科研等十多个行业的开发者广泛应用。
<div align="center">
<img src="static/docs/images/football.gif" width='800'/>
</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
- **模型丰富**: 包含**目标检测**、**实例分割**、**人脸检测**等**100+个预训练模型**，涵盖多种**全球竞赛冠军**方案
- **使用简洁**：模块化设计，解耦各个网络组件，开发者轻松搭建、试用各种检测模型及优化策略，快速得到高性能、定制化的算法。
- **端到端打通**: 从数据增强、组网、训练、压缩、部署端到端打通，并完备支持**云端**/**边缘端**多架构、多设备部署。
- **高性能**: 基于飞桨的高性能内核，模型训练速度及显存占用优势明显。支持FP16训练, 支持多机训练。
### 套件结构概览
<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</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>
#### 模型性能概览
各模型结构和骨干网络的代表模型在COCO数据集上精度mAP和单卡Tesla V100上预测速度(FPS)对比图。
<div align="center">
<img src="docs/images/fps_map.png" />
</div>
**说明：**
- `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
- `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
### 入门教程
- [安装说明](docs/tutorials/INSTALL_cn.md)
- [快速开始](docs/tutorials/QUICK_STARTED_cn.md)
- [如何准备数据](docs/tutorials/PrepareDataSet.md)
- [训练/评估/预测流程](docs/tutorials/GETTING_STARTED_cn.md)
### 进阶教程
- 参数配置
- [RCNN参数说明](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation.md)
- [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
- [数据处理模块](docs/advanced_tutorials/READER.md)
- [新增检测模型](docs/advanced_tutorials/MODEL_TECHNICAL.md)
## 模型库
- 通用目标检测:
- [模型库](docs/MODEL_ZOO_cn.md)
- [移动端模型](static/configs/mobile/README.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)
- 通用实例分割
- [SOLOv2](configs/solov2/README.md)
- 旋转框检测
- [S2ANet](configs/dota/README.md)
- 垂类领域
- [行人检测](configs/pedestrian/README.md)
- [车辆检测](configs/vehicle/README.md)
- [人脸检测](configs/face_detection/README.md)
- 比赛冠军方案
- [Objects365 2019 Challenge夺冠模型](static/docs/featured_model/champion_model/CACascadeRCNN.md)
- [Open Images 2019-Object Detction比赛最佳单模型](static/docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
## 应用案例
- [人像圣诞特效自动生成工具](static/application/christmas)
## 第三方教程推荐
- [PaddleDetection在Windows下的部署(一)](https://zhuanlan.zhihu.com/p/268657833)
- [PaddleDetection在Windows下的部署(二)](https://zhuanlan.zhihu.com/p/280206376)
- [Jetson Nano上部署PaddleDetection经验分享](https://zhuanlan.zhihu.com/p/319371293)
- [安全帽检测YOLOv3模型在树莓派上的部署](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/yolov3_for_raspi.md)
- [使用SSD-MobileNetv1完成一个项目--准备数据集到完成树莓派部署](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/ssd_mobilenet_v1_for_raspi.md)
## 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
本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
## 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。
## 引用
```
@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}
}
```
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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}
}
```
configs/ppyolo/README.md
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......@@ -187,7 +187,8 @@ Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3.
- Performance and inference spedd are measure with input shape as 608
- All models are trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset，`Box AP` is evaluation results as `mAP(IoU=0.5:0.95)`.
- Inference speed is tested on single Tesla V100 with batch size as 1 following test method and environment configuration in benchmark above.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection，see [Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO.md) for details.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.0/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection，see [Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.0/docs/MODEL_ZOO_cn.md) for details.
## Citation
......
configs/ppyolo/README_cn.md
浏览文件 @ 9c277537
......@@ -171,7 +171,7 @@ PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
- 精度与推理速度数据均为使用输入图像尺寸为608的测试结果
- Box AP为在COCO train2017数据集训练，val2017和test-dev2017数据集上评估`mAP(IoU=0.5:0.95)`数据
- 推理速度为单卡V100上，batch size=1, 使用上述benchmark测试方法的测试结果，测试环境配置为CUDA 10.2，CUDNN 7.5.1
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型，可参见[模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO.md)
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.0/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型，可参见[模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.0/docs/MODEL_ZOO_cn.md)
## 引用
......
docs/tutorials/INSTALL_cn.md
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......@@ -114,7 +114,7 @@ OK (skipped=2)
## 快速体验
**恭喜！**您已经成功安装了PaddleDetection，接下来快速体验目标检测效果
**恭喜！** 您已经成功安装了PaddleDetection，接下来快速体验目标检测效果
```
# 在GPU上预测一张图片
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