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update readme, add yoloe (#5703)
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‎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
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- 🔥 **2022.3.24：PaddleDetection发布[release/2.4版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)**
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- 发布高精度云边一体SOTA目标检测模型[PP-YOLOE](configs/ppyoloe)，COCO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。
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- 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。
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- 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。
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- 2021.11.03: PaddleDetection发布[release/2.3版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3)
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- 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。
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- 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。
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- 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。
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- 2021.08.10: PaddleDetection发布[release/2.2版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.2)
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- 发布Transformer检测系列模型，包括[DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn)。
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- 新增Dark HRNet关键点模型和MPII数据集[关键点模型](configs/keypoint)
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- 新增[人头](configs/mot/headtracking21)、[车辆](configs/mot/vehicle)跟踪垂类模型。
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- 2021.05.20: PaddleDetection发布[release/2.1版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1)
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- 新增[关键点检测](configs/keypoint)，模型包括HigherHRNet，HRNet。
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- 新增[多目标跟踪](configs/mot)能力，模型包括DeepSORT，JDE，FairMOT。
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- 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。
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- 欢迎加入PaddleDetection QQ、微信（添加并回复小助手“检测”）用户群
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48054808/157800129-2f9a0b72-6bb8-4b10-8310-93ab1639253f.jpg" width = "200" />
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<img src="https://user-images.githubusercontent.com/48054808/160531099-9811bbe6-cfbb-47d5-8bdb-c2b40684d7dd.png" width = "200" />
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- `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
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- `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)
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- `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68.9FPS
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- `PP-YOLOE`是对`PP-YOLO v2`模型的进一步优化，在COCO数据集精度51.4%，Tesla V100预测速度78.1FPS
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- 图中模型均可在[模型库](#模型库)中获取
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各移动端模型在COCO数据集上精度mAP和高通骁龙865处理器上预测速度(FPS)对比图。
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### 进阶教程
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- 参数配置
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- [RCNN参数说明](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation.md)
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- [PP-YOLO参数说明](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation.md)
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- 模型压缩(基于[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
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- [剪裁/量化/蒸馏教程](configs/slim)
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- [推理部署](deploy/README.md)
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- [模型导出教程](deploy/EXPORT_MODEL.md)
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- [Paddle Inference部署](deploy/README.md)
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- [Python端推理部署](deploy/python)
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- [推理benchmark](deploy/BENCHMARK_INFER.md)
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- 进阶开发
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- [数据处理模块](docs/advanced_tutorials/READER.md)
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- [新增检测模型](docs/advanced_tutorials/MODEL_TECHNICAL.md)
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‎README_en.md

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## <img src="https://user-images.githubusercontent.com/48054808/157793354-6e7f381a-0aa6-4bb7-845c-9acf2ecc05c3.png" width="20"/> Latest News
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- 🔥 **2022.3.24：PaddleDetection [release 2.4 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)**
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- Release GPU SOTA object detection series models (s/m/l/x) [PP-YOLOE](configs/ppyoloe), achieving mAP as 51.4% on COCO test dataset and 78.1 FPS on Nvidia V100, supporting AMP training and its training speed is 33% faster than PP-YOLOv2.
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- Release enhanced models of [PP-PicoDet](configs/picodet), including PP-PicoDet-XS model with 0.7M parameters, its mAP promoted ~2% on COCO, inference speed accelerated 63% on CPU, and post-processing integrated into the network to optimize deployment pipeline.
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- Release real-time human analysis tool [PP-Human](deploy/pphuman), which is based on data from real-life situations, supporting pedestrian detection, attribute recognition, human tracking, multi-camera tracking, human statistics and action recognition.
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## <img src="https://user-images.githubusercontent.com/48054808/157799599-e6a66855-bac6-4e75-b9c0-96e13cb9612f.png" width="20"/> Features
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- **Rich Models**
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- **Rich Models**
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PaddleDetection provides rich of models, including **250+ pre-trained models** such as **object detection**, **instance segmentation**, **face detection**, **keypoint detection**, **multi-object tracking** and etc, covering a variety of **global competition champion** schemes.
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- **Highly Flexible**
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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.
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- **Production Ready**
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- **Production Ready**
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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**.
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- **High Performance**
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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.
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## <img title="" src="https://user-images.githubusercontent.com/48054808/157800467-2a9946ad-30d1-49a9-b9db-ba33413d9c90.png" alt="" width="20"> Community
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- If you have any problem or suggestion on PaddleDetection, please send us issues through [GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues).
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- Welcome to Join PaddleDetection QQ Group and Wechat Group (reply "Det").
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<div align="center">
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<img src="https://user-images.githubusercontent.com/48054808/157800129-2f9a0b72-6bb8-4b10-8310-93ab1639253f.jpg" width = "200" />
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- `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)
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- `PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 68.9FPS on Tesla V100
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- `PP-YOLOE` is optimized version of `PP-YOLO v2` which has mAP of 51.4% and 78.1FPS on Tesla V100
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- All these models can be get in [Model Zoo](#ModelZoo)
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The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of representative mobile side models.
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### Advanced Tutorials
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- [Parameter configuration for RCNN model](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation_en.md)
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- [Parameter configuration for PP-YOLO model](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation_en.md)
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- Model Compression(Based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim))
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- [Prune/Quant/Distill](configs/slim)
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- Inference and deployment
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- [Export model for inference](deploy/EXPORT_MODEL_en.md)
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- [Exporting to ONNX and using OpenVINO for inference](docs/advanced_tutorials/openvino_inference/README.md)
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- Advanced development
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- [New data augmentations](docs/advanced_tutorials/READER_en.md)
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- [New detection algorithms](docs/advanced_tutorials/MODEL_TECHNICAL.md)
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‎docs/images/fps_map.png

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