From 376c25c0955799cbbe10628cf0fc85010dabb41a Mon Sep 17 00:00:00 2001 From: Wenyu Date: Fri, 15 Apr 2022 13:44:00 +0800 Subject: [PATCH] update readme, add yoloe (#5703) --- README_cn.md | 19 ++++++++++--------- README_en.md | 28 ++++++++++++++-------------- docs/images/fps_map.png | Bin 385390 -> 438895 bytes 3 files changed, 24 insertions(+), 23 deletions(-) diff --git a/README_cn.md b/README_cn.md index 5e30df58c..7028f83aa 100644 --- a/README_cn.md +++ b/README_cn.md @@ -17,13 +17,13 @@ ## 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),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。 - 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。 - 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。 @@ -32,13 +32,13 @@ - 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)。 @@ -71,7 +71,7 @@ - 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(添加并回复小助手“检测”)用户群 - +
@@ -244,6 +244,7 @@ - `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68.9FPS +- `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 - 图中模型均可在[模型库](#模型库)中获取 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。 @@ -269,16 +270,16 @@ ### 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) @@ -289,7 +290,7 @@ - [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) diff --git a/README_en.md b/README_en.md index ade0b769b..c9e792199 100644 --- a/README_en.md +++ b/README_en.md @@ -17,7 +17,7 @@ English | [简体中文](README_cn.md) ## Latest News - 🔥 **2022.3.24:PaddleDetection [release 2.4 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)** - + - 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. - 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. @@ -46,20 +46,20 @@ PaddleDetection is an end-to-end object detection development kit based on Paddl ## Features -- **Rich Models** - +- **Rich Models** + 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. - **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** - +- **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** - +- **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. ## Community @@ -67,7 +67,7 @@ PaddleDetection is an end-to-end object detection development kit based on Paddl - If you have any problem or suggestion on PaddleDetection, please send us issues through [GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues). - Welcome to Join PaddleDetection QQ Group and Wechat Group (reply "Det"). - +
@@ -243,7 +243,7 @@ The relationship between COCO mAP and FPS on Tesla V100 of representative 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 68.9FPS on Tesla V100 - +- `PP-YOLOE` is optimized version of `PP-YOLO v2` which has mAP of 51.4% and 78.1FPS on Tesla V100 - All these models can be get in [Model Zoo](#ModelZoo) The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of representative mobile side models. @@ -268,16 +268,16 @@ The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of represen ### Advanced Tutorials - Parameter configuration - + - [Parameter configuration for RCNN model](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation_en.md) - [Parameter configuration for PP-YOLO model](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation_en.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_en.md) - [Paddle Inference](deploy/README_en.md) - [Python inference](deploy/python) @@ -289,7 +289,7 @@ The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of represen - [Exporting to ONNX and using OpenVINO for inference](docs/advanced_tutorials/openvino_inference/README.md) - Advanced development - + - [New data augmentations](docs/advanced_tutorials/READER_en.md) - [New detection algorithms](docs/advanced_tutorials/MODEL_TECHNICAL.md) diff --git a/docs/images/fps_map.png b/docs/images/fps_map.png index d73877729c0775709e5954c008a88776bf48606a..44f3e846a877fd08fcd905027a1eced14ccb5539 100644 GIT binary patch literal 438895 zcmeEucU)81+BG67b`g+X1{9==^o~Q5uJn#lrAB%U2+pX4CL&FmQl(4p1QmkRNH??y zkzPWi6Uw)bW$t(1_nrH@Gk?#_PdSED_u2b-*0a`n5_U^VX49} z`9eZ+Y>fOk_(a_74IlV%#6w5rSCZl$#(D6MceX~VcAAf9A`} zqd&KgjX_sRN*TKZew@62)7XQAgr6PybHq1W&Kq2O(LvA1(@0Z8(i-N%f7b?PWy|mD 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