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PySODMetrics: 一份简单有效的SOD指标实现

Important

我们在这一领域的探索仍在继续，这一次是通过 PyIRSTDMetrics —— 一个源于同样核心动机的项目。 你可以把它们看作是一对“孪生”计划：这个项目旨在描绘当前评估的全景，而它的“兄弟”项目则更进一步，在此基础上加以拓展，并重新思考这一体系。 我们很希望得到你的 Star！ 🌟

介绍

一份简单有效的 SOD 指标实现。

欢迎您的改进和建议。

相关项目

  • PySODEvalToolkit: A Python-based Evaluation Toolbox for Salient Object Detection and Camouflaged Object Detection

支持的指标

Metric Sample-based Whole-based Related Class
MAE soft,si-soft MAE
S-measure $S_{m}$ soft Smeasure
weighted F-measure ($F^{\omega}_{\beta}$) soft WeightedFmeasure
Human Correction Effort Measure soft HumanCorrectionEffortMeasure
Context-Measure ($C_{\beta}$, $C^{\omega}_{\beta}$) soft ContextMeasure, CamouflageContextMeasure
Multi-Scale IoU max,avg,adp,bin MSIoU
E-measure ($E_{m}$) max,avg,adp Emeasure
F-measure (old) ($F_{\beta}$) max,avg,adp Fmeasure (Will be removed!)
F-measure (new) ($F_{\beta}$, $F_{1}$) max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+FmeasureHandler
BER max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+BERHandler
Dice max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+DICEHandler
FPR max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+FPRHandler
IoU max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+IOUHandler
Kappa max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+KappaHandler
Overall Accuracy max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+OverallAccuracyHandler
Precision max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+PrecisionHandler
Recall max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+RecallHandler
Sensitivity max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+SensitivityHandler
Specificity max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+SpecificityHandler
TNR max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+TNRHandler
TPR max,avg,adp,bin,si-max,si-avg,si-adp,si-bin bin,si-max,si-avg,si-bin FmeasureV2+TPRHandler

注意：

  • 基于样本（Sample-based）的 si- 变体会根据形状为 (num_targets, 256) 的样本级 fm 序列，计算每个样本的均值/最大值。
  • 基于整体（Whole-based）的 si- 变体会在跨所有样本的所有目标的平均 fm 序列上计算均值/最大值。
  • 由于 *adp 变体是专门为 sample-based 计算设计的，因此不支持基于整体（whole-based）的计算方式。

使用

核心文件在文件夹 py_sod_metrics 中。

  • [新，但可能不稳定] 从源代码安装：pip install git+https://github.com/lartpang/PySODMetrics.git
  • [更稳定一些] 从 PyPI 安装：pip install pysodmetrics

示例

参考

@inproceedings{Fmeasure,
    title={Frequency-tuned salient region detection},
    author={Achanta, Radhakrishna and Hemami, Sheila and Estrada, Francisco and S{\"u}sstrunk, Sabine},
    booktitle=CVPR,
    number={CONF},
    pages={1597--1604},
    year={2009}
}

@inproceedings{MAE,
    title={Saliency filters: Contrast based filtering for salient region detection},
    author={Perazzi, Federico and Kr{\"a}henb{\"u}hl, Philipp and Pritch, Yael and Hornung, Alexander},
    booktitle=CVPR,
    pages={733--740},
    year={2012}
}

@inproceedings{Smeasure,
    title={Structure-measure: A new way to evaluate foreground maps},
    author={Fan, Deng-Ping and Cheng, Ming-Ming and Liu, Yun and Li, Tao and Borji, Ali},
    booktitle=ICCV,
    pages={4548--4557},
    year={2017}
}

@inproceedings{Emeasure,
    title="Enhanced-alignment Measure for Binary Foreground Map Evaluation",
    author="Deng-Ping {Fan} and Cheng {Gong} and Yang {Cao} and Bo {Ren} and Ming-Ming {Cheng} and Ali {Borji}",
    booktitle=IJCAI,
    pages="698--704",
    year={2018}
}

@inproceedings{wFmeasure,
    title={How to evaluate foreground maps?},
    author={Margolin, Ran and Zelnik-Manor, Lihi and Tal, Ayellet},
    booktitle=CVPR,
    pages={248--255},
    year={2014}
}

@inproceedings{MSIoU,
    title = {Multiscale IOU: A Metric for Evaluation of Salient Object Detection with Fine Structures},
    author = {Ahmadzadeh, Azim and Kempton, Dustin J. and Chen, Yang and Angryk, Rafal A.},
    booktitle = ICIP,
    year = {2021},
}

@inproceedings{SizeInvarianceVariants,
    title = {Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection},
    author = {Feiran Li and Qianqian Xu and Shilong Bao and Zhiyong Yang and Runmin Cong and Xiaochun Cao and Qingming Huang},
    booktitle = ICML,
    year = {2024}
}

@inproceedings{HumanCorrectionEffortMeasure,
    title = {Highly Accurate Dichotomous Image Segmentation},
    author = {Xuebin Qin and Hang Dai and Xiaobin Hu and Deng-Ping Fan and Ling Shao and Luc Van Gool},
    booktitle = ECCV,
    year = {2022}
}

@article{ContextMeasure,
    title={Context-measure: Contextualizing Metric for Camouflage},
    author={Wang, Chen-Yang and Ji, Gepeng and Shao, Song and Cheng, Ming-Ming and Fan, Deng-Ping},
    journal={arXiv preprint arXiv:2512.07076},
    year={2025}
}