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Apache-2.0

简体中文 | English

PaddleDetection

PaddleDetection 2.0全面升级!目前默认使用动态图版本,静态图版本位于static

超高性价比PPYOLO v2和1.3M超轻量PPYOLO tiny全新出炉!欢迎使用

Anchor Free SOTA模型PAFNet发布!欢迎使用

近期活动

百度飞桨产业级目标检测技术详解系列直播课,看超越YOLOv5的PP-YOLOv2到底多强大

欢迎大家加入PPYOLOv2&Tiny技术交流群

课程安排

直播链接

  • 5月13日19:00-20:00
    • 主题: 产业级目标检测算法全解读
  • 5月14日19:00-20:00
    • 主题: 1.3M超轻量目标检测算法解读及应用
  • 5月21日20:00-21:00
    • 主题: 复杂背景下小目标检测模型开发实战

学习链接

0【PaddleDetection2.0专项】新版本快速体验

1【PaddleDetection2.0专项】如何自定义数据集

2【PaddleDetection2.0专项】快速上手PP-YOLOv2

3【PaddleDetection2.0专项】快速上手PP-YOLO tiny

4【PaddleDetection2.0专项】快速上手S2ANet

5【PaddleDetection2.0专项】快速实现行人检测

6【PaddleDetection2.0专项】快速实现人脸检测

简介

PaddleDetection飞桨目标检测开发套件,旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。

PaddleDetection模块化地实现了多种主流目标检测算法,提供了丰富的数据增强策略、网络模块组件(如骨干网络)、损失函数等,并集成了模型压缩和跨平台高性能部署能力。

经过长时间产业实践打磨,PaddleDetection已拥有顺畅、卓越的使用体验,被工业质检、遥感图像检测、无人巡检、新零售、互联网、科研等十多个行业的开发者广泛应用。

产品动态

  • 2021.04.14: 发布release/2.0版本,PaddleDetection全面支持动态图,覆盖静态图模型算法,全面升级模型效果,同时发布PP-YOLO v2, PPYOLO tiny模型,增强版anchor free模型PAFNet,新增旋转框检测S2ANet模型,详情参考PaddleDetection
  • 2021.02.07: 发布release/2.0-rc版本,PaddleDetection动态图试用版本,详情参考PaddleDetection动态图

特性

  • 模型丰富: 包含目标检测实例分割人脸检测100+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。

套件结构概览

Architectures Backbones Components Data Augmentation
  • Two-Stage Detection
    • Faster RCNN
    • FPN
    • Cascade-RCNN
    • Libra RCNN
    • Hybrid Task RCNN
    • PSS-Det
  • One-Stage Detection
    • RetinaNet
    • YOLOv3
    • YOLOv4
    • PP-YOLO
    • SSD
  • Anchor Free
    • CornerNet-Squeeze
    • FCOS
    • TTFNet
  • Instance Segmentation
    • Mask RCNN
    • SOLOv2
  • Face-Detction
    • FaceBoxes
    • BlazeFace
    • BlazeFace-NAS
  • ResNet(&vd)
  • ResNeXt(&vd)
  • SENet
  • Res2Net
  • HRNet
  • Hourglass
  • CBNet
  • GCNet
  • DarkNet
  • CSPDarkNet
  • VGG
  • MobileNetv1/v3
  • GhostNet
  • Efficientnet
  • Common
    • Sync-BN
    • Group Norm
    • DCNv2
    • Non-local
  • FPN
    • BiFPN
    • BFP
    • HRFPN
    • ACFPN
  • Loss
    • Smooth-L1
    • GIoU/DIoU/CIoU
    • IoUAware
  • Post-processing
    • SoftNMS
    • MatrixNMS
  • Speed
    • FP16 training
    • Multi-machine training
  • Resize
  • Flipping
  • Expand
  • Crop
  • Color Distort
  • Random Erasing
  • Mixup
  • Cutmix
  • Grid Mask
  • Auto Augment

模型性能概览

各模型结构和骨干网络的代表模型在COCO数据集上精度mAP和单卡Tesla V100上预测速度(FPS)对比图。

说明:

  • CBResNetCascade-Faster-RCNN-CBResNet200vd-FPN模型,COCO数据集mAP高达53.3%
  • Cascade-Faster-RCNNCascade-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
  • 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
  • 图中模型均可在模型库中获取

文档教程

入门教程

进阶教程

模型库

应用案例

第三方教程推荐

版本更新

v2.0版本已经在04/2021发布,全面支持动态图版本,新增支持BlazeFace, PSSDet等系列模型和大量骨干网络,发布PP-YOLO v2, PP-YOLO tiny和旋转框检测S2ANet模型。支持模型蒸馏、VisualDL,新增动态图预测部署benchmark,详细内容请参考版本更新文档

许可证书

本项目的发布受Apache 2.0 license许可认证。

贡献代码

我们非常欢迎你可以为PaddleDetection提供代码,也十分感谢你的反馈。

引用

@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
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简介

PaddleDetection的目的是为工业界和学术界提供丰富、易用的目标检测模型 展开 收起
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