From a5c6adbaee6b8db59595f5bfbaa637ad302c60f7 Mon Sep 17 00:00:00 2001 From: YixinKristy <576550767@qq.com> Date: Tue, 29 Mar 2022 15:08:13 +0800 Subject: [PATCH] Update Readme --- README_cn.md | 28 ++++++------ README_en.md | 126 ++++++++++++++++++++++++++++++++++----------------- 2 files changed, 97 insertions(+), 57 deletions(-) diff --git a/README_cn.md b/README_cn.md index 1b2ebaa39..72cc17c2c 100644 --- a/README_cn.md +++ b/README_cn.md @@ -2,12 +2,11 @@

- +

**飞桨目标检测开发套件，端到端地完成从训练到部署的全流程目标检测应用。** - [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) [![Version](https://img.shields.io/github/release/PaddlePaddle/PaddleDetection.svg)](https://github.com/PaddlePaddle/PaddleDetection/releases) ![python version](https://img.shields.io/badge/python-3.6+-orange.svg) @@ -19,22 +18,17 @@ - 🔥 **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](config/ppyoloe)，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)，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。 - - 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) @@ -44,6 +38,7 @@ - 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)。 @@ -54,16 +49,19 @@ #### 提供目标检测、实例分割、多目标跟踪、关键点检测等多种能力 -
- +
+
#### 应用场景覆盖工业、智慧城市、安防、交通、零售、医疗等十余种行业 -## 特性 +
+ +
+## 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。 +- **模型丰富**: 包含**目标检测**、**实例分割**、**人脸检测**、****关键点检测****、**多目标跟踪**等**250+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。 @@ -75,8 +73,8 @@ - 欢迎加入PaddleDetection QQ、微信（添加并回复小助手“检测”）用户群
- - + +
## 套件结构概览 diff --git a/README_en.md b/README_en.md index ad02eeeaf..ade0b769b 100644 --- a/README_en.md +++ b/README_en.md @@ -1,39 +1,79 @@ English | [简体中文](README_cn.md) +
+

+ +

+ +****A High-Efficient Development Toolkit for Object Detection based on [PaddlePaddle](https://github.com/paddlepaddle/paddle).**** + +[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE) +[![Version](https://img.shields.io/github/release/PaddlePaddle/PaddleDetection.svg)](https://github.com/PaddlePaddle/PaddleDetection/releases) +![python version](https://img.shields.io/badge/python-3.6+-orange.svg) +![support os](https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-yellow.svg) + +
+ +## Latest News -# Product 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. - 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). + - 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. -- 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). +- 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). -# Introduction +## Introduction PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which implements varied mainstream object detection, instance segmentation, tracking and keypoint detection algorithms in modular designwhich with configurable modules such as network components, data augmentations and losses, and release many kinds SOTA industry practice models, integrates abilities of model compression and cross-platform high-performance deployment, aims to help developers in the whole end-to-end development in a faster and better way. -### PaddleDetection provides image processing capabilities such as object detection, instance segmentation, multi-object tracking, keypoint detection and etc. +#### PaddleDetection provides image processing capabilities such as object detection, instance segmentation, multi-object tracking, keypoint detection and etc. + +
+ +
+ +#### PaddleDetection covers industrialization, smart city, security & protection, retail, medicare industry and etc. -
- +
+
+## Features -### Features +- **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. -- **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. -- **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**. -- **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. -- **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 -#### Overview of Kit Structures +- 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"). + +
+ + +
+ +## Overview of Kit Structures @@ -181,44 +221,43 @@ Based on the high performance core of PaddlePaddle, advantages of training speed -
-#### Overview of Model Performance +## Overview of Model Performance The relationship between COCO mAP and FPS on Tesla V100 of representative models of each server side architectures and backbones.
-
+
- **NOTE:** +**NOTE:** - - `CBResNet stands` for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% +- `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 +- `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` 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-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 68.9FPS on Tesla V100 - - All these models can be get in [Model Zoo](#ModelZoo) +- 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.
- +
**NOTE:** -- All data tested on Qualcomm Snapdragon 865(4\*A77 + 4\*A55) processor with batch size of 1 and CPU threads of 4, and use NCNN library in testing, benchmark scripts is publiced at [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark) +- All data tested on Qualcomm Snapdragon 865(4*A77 + 4*A55) processor with batch size of 1 and CPU threads of 4, and use NCNN library in testing, benchmark scripts is publiced at [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark) - [PP-PicoDet](configs/picodet) and [PP-YOLO-Tiny](configs/ppyolo) are developed and released by PaddleDetection, other models are not provided in PaddleDetection. -## Tutorials +## Tutorials ### Get Started @@ -226,21 +265,23 @@ The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of represen - [Prepare dataset](docs/tutorials/PrepareDataSet_en.md) - [Quick start on PaddleDetection](docs/tutorials/GETTING_STARTED.md) - ### 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) - - [C++ inference](deploy/cpp) + - [Python inference](deploy/python) + - [C++ inference](deploy/cpp) - [Paddle-Lite](deploy/lite) - [Paddle Serving](deploy/serving) - [Export ONNX model](deploy/EXPORT_ONNX_MODEL_en.md) @@ -248,14 +289,15 @@ 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_en.md) - + - [New detection algorithms](docs/advanced_tutorials/MODEL_TECHNICAL.md) -## Model Zoo +## Model Zoo - Universal object detection - [Model library and baselines](docs/MODEL_ZOO_cn.md) + - [PP-YOLOE](configs/ppyoloe/README_cn.md) - [PP-YOLO](configs/ppyolo/README.md) - [PP-PicoDet](configs/picodet/README.md) - [Enhanced Anchor Free model--TTFNet](configs/ttfnet/README_en.md) @@ -281,35 +323,35 @@ The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of represen - [Face detection](configs/face_detection/README_en.md) - [Pedestrian detection](configs/pedestrian/README.md) - [Vehicle detection](configs/vehicle/README.md) + - [Real-Time Human Analysis Tool PP-Human](deploy/pphuman) - Competition Plan - [Objects365 2019 Challenge champion model](static/docs/featured_model/champion_model/CACascadeRCNN_en.md) - [Best single model of Open Images 2019-Object Detection](static/docs/featured_model/champion_model/OIDV5_BASELINE_MODEL_en.md) -## Applications +## Applications - [Christmas portrait automatic generation tool](static/application/christmas) - [Android Fitness Demo](https://github.com/zhiboniu/pose_demo_android) -## Updates +## Updates -Updates please refer to [change log](docs/CHANGELOG_en.md) for details. +For the details of version update, please refer to [Version Update Doc](docs/CHANGELOG.md). - -## License +## License PaddleDetection is released under the [Apache 2.0 license](LICENSE). - -## Contributing +## Contribution Contributions are highly welcomed and we would really appreciate your feedback!! + - Thanks [Mandroide](https://github.com/Mandroide) for cleaning the code and unifying some function interface. - Thanks [FL77N](https://github.com/FL77N/) for contributing the code of `Sparse-RCNN` model. - Thanks [Chen-Song](https://github.com/Chen-Song) for contributing the code of `Swin Faster-RCNN` model. - Thanks [yangyudong](https://github.com/yangyudong2020), [hchhtc123](https://github.com/hchhtc123) for contributing PP-Tracking GUI interface. - Thanks [Shigure19](https://github.com/Shigure19) for contributing PP-TinyPose fitness APP. -## Citation +## Citation ``` @misc{ppdet2019, -- GitLab