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Apache-2.0

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飞桨高性能图像分割开发套件,端到端完成从训练到部署的全流程图像分割应用。

License Version python version support os stars

最新动态

  • [2023-10-29] :fire: PaddleSeg 2.9版本发布!详细发版信息请参考Release Note

    • 增加对多标签分割Multi-label segmentation,提供数据转换代码及结果可视化,实现对一系列语义分割模型的多标签分割支持。
    • 发布轻量视觉大模型MobileSAM,实现更快速的SAM推理。
    • 支持量化蒸馏训练压缩功能Quant Aware Distillation Training Compression,对PP-LiteSeg、PP-MobileSeg、OCRNet、SegFormer-B0增加量化训练压缩功能,提升推理速度。
  • [2023-09-01] :fire::fire: 飞桨AI套件 PaddleX 发布全新版本,围绕飞桨精选模型提供了一站式、全流程、高效率的开发平台,希望助力AI技术快速落地、使人人成为AI Developer!欢迎大家使用和共建。

  • [2022-04-11] PaddleSeg 2.8版本发布视觉大模型Segment Anything Model、轻量级语义分割SOTA模型PP-MobileSeg、工业质检全流程解决方案QualityInspector v0.5、通用的全景分割解决方案PanopticSeg v0.5

  • [2022-11-30] PaddleSeg 2.7版本发布实时人像抠图模型PP-MattingV2、3D医疗影像分割方案MedicalSegV2、轻量级语义分割模型RTFormer

  • [2022-07-20] PaddleSeg 2.6版本发布实时人像分割SOTA方案PP-HumanSegV2、高性能智能标注工具EISeg v1.0正式版、ImageNet分割伪标签数据预训练方法PSSL,开源PP-MattingV1代码和预训练模型。

  • [2022-04-20] PaddleSeg 2.5版本发布超轻量级语义分割模型PP-LiteSeg,高精度抠图模型PP-MattingV1,3D医疗影像开发套件MedicalSegV1,交互式分割工具EISeg v0.5。

  • [2022-01-20] PaddleSeg 2.4版本发布交互式分割工具EISeg v0.4,超轻量级人像分割方案PP-HumanSegV1,以及大规模视频会议数据集PP-HumanSeg14K

简介

PaddleSeg是基于飞桨PaddlePaddle的端到端图像分割套件,内置45+模型算法140+预训练模型,支持配置化驱动API调用开发方式,打通数据标注、模型开发、训练、压缩、部署的全流程,提供语义分割、交互式分割、Matting、全景分割四大分割能力,助力算法在医疗、工业、遥感、娱乐等场景落地应用。

特性

  • 高精度:跟踪学术界的前沿分割技术,结合高精度训练的骨干网络,提供45+主流分割网络、150+的高质量预训练模型,效果优于其他开源实现。

  • 高性能:使用多进程异步I/O、多卡并行训练、评估等加速策略,结合飞桨核心框架的显存优化功能,大幅度减少分割模型的训练开销,让开发者更低成本、更高效地完成图像分割训练。

  • 模块化:源于模块化设计思想,解耦数据准备、分割模型、骨干网络、损失函数等不同组件,开发者可以基于实际应用场景出发,组装多样化的配置,满足不同性能和精度的要求。

  • 全流程:打通数据标注、模型开发、模型训练、模型压缩、模型部署全流程,经过业务落地的验证,让开发者完成一站式开发工作。

技术交流

  • 飞桨低代码开发工具(PaddleX)—— 面向国内外主流AI硬件的飞桨精选模型一站式开发工具。包含如下核心优势:

    • 【产业高精度模型库】:覆盖10个主流AI任务 40+精选模型,丰富齐全。
    • 【特色模型产线】:提供融合大小模型的特色模型产线,精度更高,效果更好。
    • 【低代码开发模式】:图形化界面支持统一开发范式,便捷高效。
    • 【私有化部署多硬件支持】:适配国内外主流AI硬件,支持本地纯离线使用,满足企业安全保密需要。
  • PaddleX官网地址:https://aistudio.baidu.com/intro/paddlex

  • PaddleX官方交流频道:https://aistudio.baidu.com/community/channel/610

产品矩阵

模型 组件 特色案例
骨干网络
损失函数
评估指标
  • mIoU
  • Accuracy
  • Kappa
  • Dice
  • AUC_ROC
支持数据集
数据增强
  • Flipping
  • Resize
  • ResizeByLong
  • ResizeByShort
  • LimitLong
  • ResizeRangeScaling
  • ResizeStepScaling
  • Normalize
  • Padding
  • PaddingByAspectRatio
  • RandomPaddingCrop
  • RandomCenterCrop
  • ScalePadding
  • RandomNoise
  • RandomBlur
  • RandomRotation
  • RandomScaleAspect
  • RandomDistort
  • RandomAffine
分割一切模型
模型选型工具
人像分割模型
3D医疗分割模型
Cityscapes打榜模型
CVPR冠军模型
领域自适应

产业级分割模型库

高精度语义分割模型

高精度模型,分割mIoU高、推理算量大,适合部署在服务器端GPU和Jetson等设备。

模型名称 骨干网络 Cityscapes精度mIoU(%) V100 TRT推理速度(FPS) 配置文件
FCN HRNet_W18 78.97 24.43 yml
FCN HRNet_W48 80.70 10.16 yml
DeepLabV3 ResNet50_OS8 79.90 4.56 yml
DeepLabV3 ResNet101_OS8 80.85 3.2 yml
DeepLabV3 ResNet50_OS8 80.36 6.58 yml
DeepLabV3 ResNet101_OS8 81.10 3.94 yml
OCRNet :star2: HRNet_w18 80.67 13.26 yml
OCRNet HRNet_w48 82.15 6.17 yml
CCNet ResNet101_OS8 80.95 3.24 yml

测试条件:

  • V100上测速条件:针对Nvidia GPU V100,使用PaddleInference预测库的Python API,开启TensorRT加速,数据类型是FP32,输入图像维度是1x3x1024x2048。
轻量级语义分割模型

轻量级模型,分割mIoU中等、推理算量中等,可以部署在服务器端GPU、服务器端X86 CPU和移动端ARM CPU。

模型名称 骨干网络 Cityscapes精度mIoU(%) V100 TRT推理速度(FPS) 骁龙855推理速度(FPS) 配置文件
PP-LiteSeg :star2: STDC1 77.04 69.82 17.22 yml
PP-LiteSeg :star2: STDC2 79.04 54.53 11.75 yml
BiSeNetV1 - 75.19 14.67 1.53 yml
BiSeNetV2 - 73.19 61.83 13.67 yml
STDCSeg STDC1 74.74 62.24 14.51 yml
STDCSeg STDC2 77.60 51.15 10.95 yml
DDRNet_23 - 79.85 42.64 7.68 yml
HarDNet - 79.03 30.3 5.44 yml
SFNet ResNet18_OS8 78.72 10.72 - yml

测试条件:

  • V100上测速条件:针对Nvidia GPU V100,使用PaddleInference预测库的Python API,开启TensorRT加速,数据类型是FP32,输入图像维度是1x3x1024x2048。
  • 骁龙855上测速条件:针对小米9手机,使用PaddleLite预测库的CPP API,ARMV8编译,单线程,输入图像维度是1x3x256x256。
超轻量级语义分割模型

超轻量级模型,分割mIoU一般、推理算量低,适合部署在服务器端X86 CPU和移动端ARM CPU。

模型名称 骨干网络 ADE20K精度mIoU(%) 骁龙855推理延时(ms) 参数量(M) 配置文件
TopFormer-Base TopTransformer-Base 38.28 480.6 5.13 config
PP-MobileSeg-Base StrideFormer-Base 41.57 265.5 5.62 config
TopFormer-Tiny TopTransformer-Tiny 32.46 490.3 1.41 config
PP-MobileSeg-Tiny StrideFormer-Tiny 36.39 215.3 1.61 config

测试条件:

  • 针对小米9手机,使用PaddleLite预测库的CPP API,ARMV8编译,单线程,输入图像维度是1x3x512x512。测试模型在带有最后一个argmax算子的条件下进行测试。
模型名称 骨干网络 Cityscapes精度mIoU(%) V100 TRT推理速度(FPS) 骁龙855推理速度(FPS) 配置文件
MobileSeg MobileNetV2 73.94 67.57 27.01 yml
MobileSeg :star2: MobileNetV3 73.47 67.39 32.90 yml
MobileSeg Lite_HRNet_18 70.75 10.5 13.05 yml
MobileSeg ShuffleNetV2_x1_0 69.46 37.09 39.61 yml
MobileSeg GhostNet_x1_0 71.88 35.58 38.74 yml

测试条件:

  • V100上测速条件:针对Nvidia GPU V100,使用PaddleInference预测库的Python API,开启TensorRT加速,数据类型是FP32,输入图像维度是1x3x1024x2048。
  • 骁龙855上测速条件:针对小米9手机,使用PaddleLite预测库的CPP API,ARMV8编译,单线程,输入图像维度是1x3x256x256。

使用教程

入门教程

基础教程

进阶教程

欢迎贡献

特色能力

产业实践范例

更多范例项目可参考:『图像分割经典项目集』用PaddleSeg能做什么?

许可证书

本项目的发布受Apache 2.0 license许可认证。

社区贡献

  • 非常感谢jm12138贡献U2-Net模型。
  • 非常感谢zjhellofss(傅莘莘)贡献Attention U-Net模型,和Dice loss损失函数。
  • 非常感谢liuguoyu666贡献U-Net++模型。
  • 非常感谢yazheng0307 (刘正)贡献快速开始教程文档。
  • 非常感谢CuberrChen贡献STDC (rethink BiSeNet) PointRend,和 Detail Aggregate损失函数。
  • 非常感谢stuartchen1949贡献 SegNet。
  • 非常感谢justld(郎督)贡献 UPerNet, DDRNet, CCNet, ESPNetV2, DMNet, ENCNet, HRNet_W48_Contrast, BiSeNetV1, FastFCN, SECrossEntropyLoss 和PixelContrastCrossEntropyLoss。
  • 非常感谢Herman-Hu-saber(胡慧明)参与贡献 ESPNetV2。
  • 非常感谢zhangjin12138贡献数据增强方法 RandomCenterCrop。
  • 非常感谢simuler 贡献 ESPNetV1。
  • 非常感谢ETTR123(张恺) 贡献 ENet,PFPNNet。

学术引用

如果我们的项目在学术上帮助到你,请考虑以下引用:

@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 Authors},
    howpublished = {\url{https://github.com/PaddlePaddle/PaddleSeg}},
    year={2019}
}
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简介

End-to-End Image Segmentation Suite Based on PaddlePaddle. (『飞桨』图像分割开发套件) 展开 收起
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