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Add industrial practice tutorial #6067

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update readme
YixinKristy May 26, 2022
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22 changes: 13 additions & 9 deletions README_cn.md
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## <img src="https://user-images.githubusercontent.com/48054808/157793354-6e7f381a-0aa6-4bb7-845c-9acf2ecc05c3.png" width="20"/> 产品动态

- 🔥 **2022.3.24：PaddleDetection发布[release/2.4版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)**

- 发布高精度云边一体SOTA目标检测模型[PP-YOLOE](configs/ppyoloe)，发布s/m/l/x版本，l版本COCO test2017数据集精度51.4%，V100预测速度78.1 FPS，支持混合精度训练，训练较PP-YOLOv2加速33%，全系列多尺度模型，满足不同硬件算力需求，可适配服务器、边缘端GPU及其他服务器端AI加速卡。
- 发布边缘端和CPU端超轻量SOTA目标检测模型[PP-PicoDet增强版](configs/picodet)，精度提升2%左右，CPU预测速度提升63%，新增参数量0.7M的PicoDet-XS模型，提供模型稀疏化和量化功能，便于模型加速，各类硬件无需单独开发后处理模块，降低部署门槛。
- 发布实时行人分析工具[PP-Human](deploy/pphuman)，支持行人跟踪、人流量统计、人体属性识别与摔倒检测四大能力，基于真实场景数据特殊优化，精准识别各类摔倒姿势，适应不同环境背景、光线及摄像角度。
- 新增[YOLOX](configs/yolox)目标检测模型，支持nano/tiny/s/m/l/x版本，x版本COCO val2017数据集精度51.8%。

- 2021.11.03: PaddleDetection发布[release/2.3版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3)

- 发布轻量级检测特色模型⚡[PP-PicoDet](configs/picodet)，0.99m的参数量可实现精度30+mAP、速度150FPS。
- 发布轻量级关键点特色模型⚡[PP-TinyPose](configs/keypoint/tiny_pose)，单人场景FP16推理可达122FPS、51.8AP，具有精度高速度快、检测人数无限制、微小目标效果好的优势。
- 发布实时跟踪系统[PP-Tracking](deploy/pptracking)，覆盖单、多镜头下行人、车辆、多类别跟踪，对小目标、密集型特殊优化，提供人、车流量技术解决方案。
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- 新增轻量化关键点模型[Lite HRNet](configs/keypoint)关键点模型并支持Paddle Lite部署。

- 2021.08.10: PaddleDetection发布[release/2.2版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.2)

- 发布Transformer检测系列模型，包括[DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn)。
- 新增Dark HRNet关键点模型和MPII数据集[关键点模型](configs/keypoint)
- 新增[人头](configs/mot/headtracking21)、[车辆](configs/mot/vehicle)跟踪垂类模型。

- 2021.05.20: PaddleDetection发布[release/2.1版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1)

- 新增[关键点检测](configs/keypoint)，模型包括HigherHRNet，HRNet。
- 新增[多目标跟踪](configs/mot)能力，模型包括DeepSORT，JDE，FairMOT。
- 发布PPYOLO系列模型压缩模型，新增[ONNX模型导出教程](deploy/EXPORT_ONNX_MODEL.md)。
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- 如果你发现任何PaddleDetection存在的问题或者是建议, 欢迎通过[GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues)给我们提issues。

- 欢迎加入PaddleDetection QQ、微信用户群（添加并回复小助手“检测”）

<div align="center">
<img src="https://user-images.githubusercontent.com/48054808/157800129-2f9a0b72-6bb8-4b10-8310-93ab1639253f.jpg" width = "200" />
<img src="https://user-images.githubusercontent.com/48054808/160531099-9811bbe6-cfbb-47d5-8bdb-c2b40684d7dd.png" width = "200" />
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### 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)
- [Paddle Inference部署](deploy/README.md)
- [Python端推理部署](deploy/python)
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- [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)

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- [基于YOLOv3实现跌倒检测 ](https://aistudio.baidu.com/aistudio/projectdetail/2500639)

- [基于PP-PicoDetv2 的路面垃圾检测](https://aistudio.baidu.com/aistudio/projectdetail/3846170?channelType=0&channel=0)
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加下范例库首页链接吧

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嗯嗯已加


- [基于人体关键点检测的合规检测](https://aistudio.baidu.com/aistudio/projectdetail/4061642?contributionType=1)

## <img src="https://user-images.githubusercontent.com/48054808/157829890-a535b8a6-631c-4c87-b861-64d4b32b2d6a.png" width="20"/> 模型库

- 通用目标检测:
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25 changes: 25 additions & 0 deletions industrial_tutorial /README_cn.md
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简体中文 | [English](README_en.md)

# 产业实践范例

为了缩小基础理论教学与产业落地间的差距，PaddleDetection联合产业头部企业，结合实际经验，选取经典场景，提供了从**数据准备、模型训练优化，到模型部署的全流程可复用方案**，降低产业落地门槛，让大家在真实数据环境下深入地了解这些案例，获取产业实现方案。

<div align="center">
<img src="https://user-images.githubusercontent.com/48054808/167805539-2c9f025e-d52c-4ec1-a8fc-ec19651512e5.png" width="800"/>
</div>

## 范例列表

- [基于PP-PicoDet的通信塔识别及Android端部署](./communication_tower_detection/README_cn.md)

- [基于Faster-RCNN的瓷砖表面瑕疵检测](./tile_blemish_detection/README_cn.md)

- [基于PaddleDetection的PCB瑕疵检测](./PCB_blemish_detection/README_cn.md)

- [基于FairMOT实现人流量统计](./pedestrain_volume/README_cn.md)

- [基于YOLOv3实现跌倒检测](./falling_down_detection/README_cn.md)

- [基于PP-PicoDetv2 的路面垃圾检测](./)

*范例将持续更新中