https://github.com/cisco08/paddleseg

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
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Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
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Host: GitHub
Owner: cisco08
License: apache-2.0
Default Branch: release/2.5
Homepage: https://arxiv.org/abs/2101.06175
Size: 337 MB
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Created about 4 years ago · Last pushed about 4 years ago
https://github.com/cisco08/PaddleSeg/blob/release/2.5/

English | [](README_CN.md)




  


**A High-Efficient Development Toolkit for Image Segmentation based on [PaddlePaddle](https://github.com/paddlepaddle/paddle).**

[![Build Status](https://travis-ci.org/PaddlePaddle/PaddleSeg.svg?branch=release/2.1)](https://travis-ci.org/PaddlePaddle/PaddleSeg)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
[![Version](https://img.shields.io/github/release/PaddlePaddle/PaddleSeg.svg)](https://github.com/PaddlePaddle/PaddleSeg/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)





## News 

  [2022-04-20] :fire: PaddleSeg v2.5 is released! More details in Release Notes.
    
        Release PP-LiteSeg, a real-time semantic segmentation model. It achieves SOTA trade-off between segmentation accuracy and inference speed. [techical report]
        Release PP-Matting, a trimap-free image matting model for extremely fine-grained segmentation. It achieves SOTA performance on Composition-1k and Distinctions-646. [techical report]
        Release MedicalSeg, a newly easy-to-use toolkit for 3D medical image segmentation. It supports the whole process including data preprocessing, model training, and model deployment, and provides the high-accuracy models on lung and spine segmentation.
        
Upgrade the interactive annotation tool EISeg v0.5 with supporting new areas in chest X-Ray, MRI spine, and defect inspection.
        Add 5 semantic segmentatioin models, including variants of PP-LiteSeg.
    
 [2022-01-20] We release PaddleSeg v2.4 with EISeg v0.4, and PP-HumanSeg including open-sourced dataset PP-HumanSeg14K. 
 [2021-10-11] We released PaddleSeg v2.3 with the improved interactive segmentation tool EISeg v0.3, two matting algorithms, and segmentation model compression.




## Introduction

PaddleSeg is an end-to-end high-efficent development toolkit for image segmentation based on PaddlePaddle, which  helps both developers and researchers in the whole process of designing segmentation models, training models, optimizing performance and inference speed, and deploying models. A lot of well-trained models and various real-world applications in both industry and academia help users conveniently build hands-on experiences in image segmentation.

* #### Four segmentation areas: semantic segmentation, interactive segmentation, panoptic segmentation and image matting.


  



---------------

 * #### Various applications in autonomous driving, medical segmentation, remote sensing, quality inspection, and other scenarios.


  



---------------


## Features

*  **High-Performance Model**: Based on the high-performance backbone trained by semi-supervised label knowledge distillation scheme ([SSLD]((https://paddleclas.readthedocs.io/zh_CN/latest/advanced_tutorials/distillation/distillation.html#ssld))), combined with the state of the art segmentation technology, we provide 80+ high-quality pre-training models, which are better than other open-source implementations.

*  **Modular Design**: PaddleSeg supports 40+ mainstream *segmentation networks*, developers can start based on actual application scenarios and assemble diversified training configurations combined with modular design of *data enhancement strategies*, *backbone networks*, *loss functions* and other different components to meet different performance and accuracy requirements.

*  **High Efficiency**: PaddleSeg provides multi-process asynchronous I/O, multi-card parallel training, evaluation, and other acceleration strategies, combined with the memory optimization function of the PaddlePaddle, which can greatly reduce the training overhead of the segmentation model, all this allowing developers to lower cost and more efficiently train image segmentation model.



## Overview 


  
    
      
        Models
      
      
        Components
      
      
        Projects
      
    
    
      
            ANN
            BiSeNetV2
            DANet
            DeepLabV3
            DeepLabV3P
            Fast-SCNN
            HRNet-FCN
            GCNet
            GSCNN
            HarDNet
            OCRNet
            PSPNet
            U-Net
            U²-Net
            Att U-Net
            U-Net++
            U-Net3+
            DecoupledSeg
            EMANet
            ISANet
            DNLNet
            SFNet
            PP-HumanSeg
            PortraitNet
            STDC
            GINet
            PointRend
            SegNet
            ESPNetV2
            HRNet-Contrast
            DMNet
            ESPNetV1
            ENCNet
            PFPNNet
            FastFCN
            BiSeNetV1
            SETR
            MLA Transformer
            SegFormer
            SegMenter
            ENet
            CCNet
            DDRNet
            GloRe
            PP-LiteSeg :star:
      
      
        Backbones

          
            HRNet
            MobileNetV2
            MobileNetV3
            ResNet
            STDCNet
            XCeption
            VIT
            MixVIT
            Swin Transformer
            
        Losses

          
            Cross Entropy
            Binary CE
            Bootstrapped CE
            Point CE
            OHEM CE
            Pixel Contrast CE
            Focal
            Dice
            RMI
            KL
            L1
            Lovasz
            MSE
            Edge Attention
            Relax Boundary
            Connectivity
            MultiClassFocal
          
        Metrics

          
            mIoU
            Accuracy
            Kappa
            Dice
            AUC_ROC
            
      
      
        Datasets

          
            Cityscapes
            Pascal VOC
            ADE20K  
            Pascal Context  
            COCO Stuff
            SUPERVISELY
            EG1800
            CHASE_DB1
            HRF
            DRIVE
            STARE
            PP-HumanSeg14K
          
        Data Augmentation

        
          Flipping  
          Resize  
          ResizeByLong
          ResizeByShort
          LimitLong  
          ResizeRangeScaling  
          ResizeStepScaling
          Normalize
          Padding
          PaddingByAspectRatio
          RandomPaddingCrop  
          RandomCenterCrop
          ScalePadding
          RandomNoise  
          RandomBlur  
          RandomRotation  
          RandomScaleAspect  
          RandomDistort  
          RandomAffine  
          
      
      
        Interactive Segmentation

          
            EISeg
            RITM
            EdgeFlow
           
       Image Matting

        
            PP-Matting
            DIM
            MODNet
            PP-HumanMatting
        
        Human Segmentation

        
            PP-HumanSeg
        
        3D Medical Segmentation

        
          VNet
        
        Cityscapes SOTA

        
            HMSA
        
        Panoptic Segmentation

          
            Panoptic-DeepLab
          
        CVPR Champion

        
            MLA Transformer
        
        Domain Adaption

        
            PixMatch
        
        
    



    
  


## Model Zoo

The relationship between mIoU and FLOPs of representative architectures and backbones. See [Model Zoo Overview](./docs/model_zoo_overview.md) for more details.


  




## Tutorials 

* [Installation Guide](./docs/install.md)
* [Quick Start](./docs/whole_process.md)

*  Data Preparation
   * [Annotated Data Preparation](./docs/data/marker/marker.md)
   * [Annotation Tutorial](./docs/data/transform/transform.md)
   * [Custom Dataset](./docs/data/custom/data_prepare.md)

* [Model Training](/docs/train/train.md)
* [Model Evaluation](./docs/evaluation/evaluate/evaluate.md)
* [Prediction](./docs/predict/predict.md)

* Model Export
    * [Export Inference Model](./docs/model_export.md)
    * [Export ONNX Model](./docs/model_export_onnx.md)

*  Model Deploy
    * [Paddle Inference (Python)](./docs/deployment/inference/python_inference.md)
    * [Paddle Inference (C++)](./docs/deployment/inference/cpp_inference.md)
    * [Paddle Lite](./docs/deployment/lite/lite.md)
    * [Paddle Serving](./docs/deployment/serving/serving.md)
    * [Paddle JS](./docs/deployment/web/web.md)
    * [Benchmark](./docs/deployment/inference/infer_benchmark.md)

*  Model Compression
    * [Quantization](./docs/slim/quant/quant.md)
    * [Distillation](./docs/slim/distill/distill.md)
    * [Prune](./docs/slim/prune/prune.md)

*  Easy API
    * [API Documention](./docs/apis/README.md)
    * [API Tutorial](./docs/api_example.md)
*  Baisc Knowledge
    * [Data Augmentation](./docs/module/data/data.md)
    * [Loss Description](./docs/module/loss/losses_en.md)
*  Advanced Development
    * [Detailed Configuration File](./docs/design/use/use.md)
    * [Create Your Own Model](./docs/design/create/add_new_model.md)
*  Pull Request
    * [PR Tutorial](./docs/pr/pr/pr.md)
    * [PR Style](./docs/pr/pr/style_cn.md)

* [Static Graph Version](./docs/static/static.md)
* [Community](#Community)
* [FAQ](./docs/faq/faq/faq.md)

## Practical Projects
  * [Interactive Segmentation](./EISeg)
  * [Image Matting](./Matting)
  * [PP-HumanSeg](./contrib/PP-HumanSeg)
  * [3D ](./contrib/MedicalSeg)
  * [Cityscapes SOTA](./contrib/CityscapesSOTA)
  * [Panoptic Segmentation](./contrib/PanopticDeepLab)
  * [CVPR Champion Solution](./contrib/AutoNUE)
  * [Domain Adaptation](./contrib/DomainAdaptation)


## Community 

* If you have any problem or suggestion on PaddleSeg, please send us issues through [GitHub Issues](https://github.com/PaddlePaddle/PaddleSeg/issues).
* Welcome to Join PaddleSeg WeChat Group

  


## License

PaddleSeg is released under the [Apache 2.0 license](LICENSE).

## Acknowledgement
* Thanks [jm12138](https://github.com/jm12138) for contributing U²-Net.
* Thanks [zjhellofss](https://github.com/zjhellofss) (Fu Shenshen) for contributing Attention U-Net, and Dice Loss.
* Thanks [liuguoyu666](https://github.com/liguoyu666), [geoyee](https://github.com/geoyee) for contributing U-Net++ and U-Net3+.
* Thanks [yazheng0307](https://github.com/yazheng0307) (LIU Zheng) for contributing quick-start document.
* Thanks [CuberrChen](https://github.com/CuberrChen) for contributing STDC(rethink BiSeNet), PointRend and DetailAggregateLoss.
* Thanks [stuartchen1949](https://github.com/stuartchen1949) for contributing SegNet.
* Thanks [justld](https://github.com/justld) (Lang Du) for contributing DDRNet, CCNet, ESPNetV2, DMNet, ENCNet, HRNet_W48_Contrast, FastFCN, BiSeNetV1, SECrossEntropyLoss and PixelContrastCrossEntropyLoss.
* Thanks [Herman-Hu-saber](https://github.com/Herman-Hu-saber) (Hu Huiming) for contributing ESPNetV2.
* Thanks [zhangjin12138](https://github.com/zhangjin12138) for contributing RandomCenterCrop.
* Thanks [simuler](https://github.com/simuler) for contributing ESPNetV1.
* Thanks [ETTR123](https://github.com/ETTR123)(Zhang Kai) for contributing ENet, PFPNNet.

## Citation
If you find our project useful in your research, please consider citing:

```latex
@misc{liu2021paddleseg,
      title={PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation},
      author={Yi Liu and Lutao Chu and Guowei Chen and Zewu Wu and Zeyu Chen and Baohua Lai and Yuying Hao},
      year={2021},
      eprint={2101.06175},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{paddleseg2019,
    title={PaddleSeg, End-to-end image segmentation kit based on PaddlePaddle},
    author={PaddlePaddle Contributors},
    howpublished = {\url{https://github.com/PaddlePaddle/PaddleSeg}},
    year={2019}
}
```