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A High-Efficient Development Toolkit for Object Detection based on PaddlePaddle.

Latest News

  • 🔥 2022.3.24:PaddleDetection release 2.4 version

    • Release GPU SOTA object detection series models (s/m/l/x) PP-YOLOE, supporting s/m/l/x version, achieving mAP as 51.4% 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, 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, 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, supporting nano/tiny/s/m/l/x version, achieving mAP as 51.8% on COCO val dataset by YOLOX-x.
  • 2021.11.03: Release release/2.3 version. Release mobile object detection model ⚡PP-PicoDet, mobile keypoint detection model ⚡PP-TinyPose,Real-time tracking system PP-Tracking. Release object detection models, including Swin-Transformer, TOOD, GFL, release Sniper tiny object detection models and optimized PP-YOLO-EB model for EdgeBoard. Release mobile keypoint detection model Lite HRNet.

  • 2021.08.10: Release release/2.2 version. Release Transformer object detection models, including DETR, Deformable DETR, Sparse RCNN. Release keypoint detection models, including DarkHRNet and model trained on MPII dataset. Release head-tracking and vehicle-tracking multi-object tracking models.

  • 2021.05.20: Release release/2.1 version. Release Keypoint Detection, including HigherHRNet and HRNet, Multi-Object Tracking, including DeepSORT,JDE and FairMOT. Release model compression for PPYOLO series models.Update documents such as EXPORT ONNX MODEL.


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 design with configurable modules such as network components, data augmentations and losses. It releases many kinds SOTA industry practice models and integrates abilities of model compression and cross-platform high-performance deployment to help developers in the whole process with a faster and better way.

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.


  • 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

    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.


  • If you have any problem or suggestion on PaddleDetection, please send us issues through GitHub Issues.

  • Welcome to Join PaddleDetection QQ Group and Wechat Group (reply "Det").

Overview of Kit Structures

Architectures Backbones Components Data Augmentation
  • Object Detection
    • Faster RCNN
    • FPN
    • Cascade-RCNN
    • Libra RCNN
    • Hybrid Task RCNN
    • PSS-Det
    • RetinaNet
    • YOLOv3
    • YOLOv4
    • PP-YOLOv1/v2
    • PP-YOLO-Tiny
    • PP-YOLOE
    • YOLOX
    • SSD
    • CornerNet-Squeeze
    • FCOS
    • TTFNet
    • PP-PicoDet
    • DETR
    • Deformable DETR
    • Swin Transformer
    • Sparse RCNN
  • Instance Segmentation
    • Mask RCNN
    • SOLOv2
  • Face Detection
    • FaceBoxes
    • BlazeFace
    • BlazeFace-NAS
  • Multi-Object-Tracking
    • JDE
    • FairMOT
    • DeepSORT
  • KeyPoint-Detection
    • HRNet
    • HigherHRNet
  • ResNet(&vd)
  • ResNeXt(&vd)
  • SENet
  • Res2Net
  • HRNet
  • Hourglass
  • CBNet
  • GCNet
  • DarkNet
  • CSPDarkNet
  • VGG
  • MobileNetv1/v3
  • GhostNet
  • Efficientnet
  • BlazeNet
  • Common
    • Sync-BN
    • Group Norm
    • DCNv2
    • Non-local
  • KeyPoint
    • DarkPose
  • FPN
    • BiFPN
    • BFP
    • HRFPN
    • ACFPN
  • Loss
    • Smooth-L1
    • GIoU/DIoU/CIoU
    • IoUAware
  • Post-processing
    • SoftNMS
    • MatrixNMS
  • Speed
    • FP16 training
    • Multi-machine training
  • Resize
  • Lighting
  • Flipping
  • Expand
  • Crop
  • Color Distort
  • Random Erasing
  • Mixup
  • Mosaic
  • AugmentHSV
  • Cutmix
  • Grid Mask
  • Auto Augment
  • Random Perspective

Overview of Model Performance

The relationship between COCO mAP and FPS on Tesla V100 of representative models of each server side architectures and backbones.


  • 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

  • PP-YOLO achieves mAP of 45.9% on COCO and 72.9FPS on Tesla V100. Both precision and speed surpass YOLOv4

  • 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

The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of representative mobile side models.


  • All data tested on Qualcomm Snapdragon 865(4A77 + 4A55) processor with batch size of 1 and CPU threads of 4, and use NCNN library in testing, benchmark scripts is publiced at MobileDetBenchmark
  • PP-PicoDet and PP-YOLO-Tiny are developed and released by PaddleDetection, other models are not provided in PaddleDetection.


Get Started

Advanced Tutorials

Model Zoo



For the details of version update, please refer to Version Update Doc.


PaddleDetection is released under the Apache 2.0 license.


Contributions are highly welcomed and we would really appreciate your feedback!!

  • Thanks Mandroide for cleaning the code and unifying some function interface.
  • Thanks FL77N for contributing the code of Sparse-RCNN model.
  • Thanks Chen-Song for contributing the code of Swin Faster-RCNN model.
  • Thanks yangyudong, hchhtc123 for contributing PP-Tracking GUI interface.
  • Thanks Shigure19 for contributing PP-TinyPose fitness APP.
  • Thanks manangoel99 for contributing Wandblogger for visualization of the training and evaluation metrics


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

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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 expand collapse


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