From 35e07c1468873945acb920221a527a6b469c9ea6 Mon Sep 17 00:00:00 2001 From: wangguanzhong Date: Wed, 10 Aug 2022 21:16:25 +0800 Subject: [PATCH] cherry-pick update qr code (#6627) * update qq qr-code, test=document_fix * update qq qr code, test=document_fix --- README_cn.md | 42 +++++++++++++++++------------------------- README_en.md | 36 ++++++++++++++++++++++++++---------- 2 files changed, 43 insertions(+), 35 deletions(-) diff --git a/README_cn.md b/README_cn.md index 7e6ddacd1..510cb9e6f 100644 --- a/README_cn.md +++ b/README_cn.md @@ -16,45 +16,37 @@

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+ ## 产品动态 -- 🔥 **2022.8.01：发布[PP-TinyPose升级版](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/keypoint/tiny_pose). 在健身、舞蹈等场景的业务数据集端到端AP提升9.1** +- 🔥 **2022.8.09：[YOLO家族全系列模型](https://github.com/nemonameless/PaddleDetection_YOLOSeries)发布** + - 全面覆盖的YOLO家族经典与最新模型: 包括YOLOv3，百度飞桨自研的实时高精度目标检测检测模型PP-YOLOE，以及前沿检测算法YOLOv4、YOLOv5、YOLOX，MT-YOLOv6及YOLOv7 + - 更强的模型性能：基于各家前沿YOLO算法进行创新并升级，缩短训练周期5~8倍，精度普遍提升1%~5% mAP；使用模型压缩策略实现精度无损的同时速度提升30%以上 + - 完备的端到端开发支持：支持从模型训练、评估、预测到模型量化压缩，部署多种硬件的端到端开发全流程。同时支持不同模型算法灵活切换，一键实现算法二次开发 + +- 🔥 **2022.8.01：发布[PP-TinyPose升级版](./configs/keypoint/tiny_pose/). 在健身、舞蹈等场景的业务数据集端到端AP提升9.1** - 新增体育场景真实数据，复杂动作识别效果显著提升，覆盖侧身、卧躺、跳跃、高抬腿等非常规动作 - 检测模型采用[PP-PicoDet增强版](./configs/picodet/README.md)，在COCO数据集上精度提升3.1% - 关键点稳定性增强，新增滤波稳定方式，使得视频预测结果更加稳定平滑 -- 🔥 **2022.7.14：[行人分析工具PP-Human v2](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/pipeline)发布** +- 2022.7.14：[行人分析工具PP-Human v2](./deploy/pipeline)发布 - 四大产业特色功能：高性能易扩展的五大复杂行为识别、闪电级人体属性识别、一行代码即可实现的人流检测与轨迹留存以及高精度跨镜跟踪 - 底层核心算法性能强劲：覆盖行人检测、跟踪、属性三类核心算法能力，对目标人数、光线、背景均无限制 - 极低使用门槛：提供保姆级全流程开发及模型优化策略、一行命令完成推理、兼容各类数据输入格式 - 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51.6%，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。 + - 发布高精度云边一体SOTA目标检测模型[PP-YOLOE](configs/ppyoloe)，提供s/m/l/x版本，l版本COCO test2017数据集精度51.6%，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。 + - 发布实时行人分析工具[PP-Human](deploy/pipeline)，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。 - - 新增[Swin Transformer](configs/faster_rcnn)，[TOOD](configs/tood)，[GFL](configs/gfl)目标检测模型。 - - 发布[Sniper](configs/sniper)小目标检测优化模型，发布针对EdgeBoard优化[PP-YOLO-EB](configs/ppyolo)模型。 - - 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) +- [更多版本发布](https://github.com/PaddlePaddle/PaddleDetection/releases) - - 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)。 ## 简介 @@ -86,7 +78,7 @@ - 欢迎加入PaddleDetection QQ、微信用户群（添加并回复小助手“检测”）
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diff --git a/README_en.md b/README_en.md index 44fa4d759..cdb317920 100644 --- a/README_en.md +++ b/README_en.md @@ -16,20 +16,36 @@ English | [简体中文](README_cn.md) +
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+ ## Latest News -- 🔥 **2022.3.24：PaddleDetection [release 2.4 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)** +- 🔥 **2022.8.09：Release [YOLO series model zoo](https://github.com/nemonameless/PaddleDetection_YOLOSeries)** + - Comprehensive coverage of classic and latest models of the YOLO series: Including YOLOv3，Paddle real-time object detection model PP-YOLOE, and frontier detection algorithms YOLOv4, YOLOv5, YOLOX, MT-YOLOv6 and YOLOv7 + - Better model performance：Upgrade based on various YOLO algorithms, shorten training time in 5-8 times and the accuracy is generally improved by 1%-5% mAP. The model compression strategy is used to achieve 30% improvement in speed without precision loss + - Complete end-to-end development support：End-to-end development pipieline including training, evaluation, inference, model compression and deployment on various hardware. Meanwhile, support flexible algorithnm switch and implement customized development efficiently - - Release GPU SOTA object detection series models (s/m/l/x) [PP-YOLOE](configs/ppyoloe), supporting s/m/l/x version, achieving mAP as 51.6% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, supporting AMP training and its training speed is 33% faster than PP-YOLOv2. - - 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. - - 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. - - Release [YOLOX](configs/yolox), supporting nano/tiny/s/m/l/x version, achieving mAP as 51.8% on COCO val dataset by YOLOX-x. +- 🔥 **2022.8.01：Release [PP-TinyPose plus](./configs/keypoint/tiny_pose/). The end-to-end precision improves 9.1% AP in dataset + of fitness and dance scenes** + - Increase data of sports scenes, and the recognition performance of complex actions is significantly improved, covering actions such as sideways, lying down, jumping, and raising legs + - Detection model uses PP-PicoDet plus and the precision on COCO dataset is improved by 3.1% mAP + - The stability of keypoints is enhanced. Implement the filter stabilization method to make the video prediction result more stable and smooth. -- 2021.11.03: Release [release/2.3](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.3) version. Release mobile object detection model ⚡[PP-PicoDet](configs/picodet), mobile keypoint detection model ⚡[PP-TinyPose](configs/keypoint/tiny_pose)，Real-time tracking system [PP-Tracking](deploy/pptracking). Release object detection models, including [Swin-Transformer](configs/faster_rcnn), [TOOD](configs/tood), [GFL](configs/gfl), release [Sniper](configs/sniper) tiny object detection models and optimized [PP-YOLO-EB](configs/ppyolo) model for EdgeBoard. Release mobile keypoint detection model [Lite HRNet](configs/keypoint). +- 2022.7.14：Release [pedestrian analysis tool PP-Human v2](./deploy/pipeline) + - Four major functions: five complicated action recognition with high performance and Flexible, real-time human attribute recognition, visitor flow statistics and high-accuracy multi-camera tracking. + - High performance algorithm: including pedestrian detection, tracking, attribute recognition which is robust to the number of targets and the variant of background and light. + - Highly Flexible: providing complete introduction of end-to-end development and optimization strategy, simple command for deployment and compatibility with different input format. -- 2021.08.10: Release [release/2.2](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.2) version. Release Transformer object detection models, including [DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn). Release [keypoint detection](configs/keypoint) models, including DarkHRNet and model trained on MPII dataset. Release [head-tracking](configs/mot/headtracking21) and [vehicle-tracking](configs/mot/vehicle) multi-object tracking models. +- 2022.3.24：PaddleDetection released[release/2.4 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4) + - Release high-performanace SOTA object detection model [PP-YOLOE](configs/ppyoloe). It integrates cloud and edge devices and provides S/M/L/X versions. In particular, Verson L has the accuracy as 51.4% on COCO test 2017 dataset, inference speed as 78.1 FPS on a single Test V100. It supports mixed precision training, 33% faster than PP-YOLOv2. Its full range of multi-sized models can meet different hardware arithmetic requirements, and adaptable to server, edge-device GPU and other AI accelerator cards on servers. + - Release ultra-lightweight SOTA object detection model [PP-PicoDet Plus](configs/picodet) with 2% improvement in accuracy and 63% improvement in CPU inference speed. Add PicoDet-XS model with a 0.7M parameter, providing model sparsification and quantization functions for model acceleration. No specific post processing module is required for all the hardware, simplifying the deployment. + - Release the real-time pedestrian analysis tool [PP-Human](deploy/pphuman). It has four major functions: pedestrian tracking, visitor flow statistics, human attribute recognition and falling detection. For falling detection, it is optimized based on real-life data with accurate recognition of various types of falling posture. It can adapt to different environmental background, light and camera angle. + - Add [YOLOX](configs/yolox) object detection model with nano/tiny/S/M/L/X. X version has the accuracy as 51.8% on COCO Val2017 dataset. -- 2021.05.20: Release [release/2.1](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.1) version. Release [Keypoint Detection](configs/keypoint), including HigherHRNet and HRNet, [Multi-Object Tracking](configs/mot), including DeepSORT，JDE and FairMOT. Release model compression for PPYOLO series models.Update documents such as [EXPORT ONNX MODEL](deploy/EXPORT_ONNX_MODEL.md). +- [More releases](https://github.com/PaddlePaddle/PaddleDetection/releases) ## Introduction @@ -72,8 +88,8 @@ PaddleDetection is an end-to-end object detection development kit based on Paddl - Welcome to Join PaddleDetection QQ Group and Wechat Group (reply "Det").
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## Overview of Kit Structures -- GitLab