Awesome
PyADMetric_EvalToolkit (PyAD_Metric): A Python-based Simple yet Efficient Evaluation Toolbox for Anomaly Detection-like tasks.
Installation
pip install -r requirements.txt
Getting Started
python test_score.py
2D Anomaly Detection
AUROC: Area Under the Receiver Operating Characteristic Curve
<p align="center"> <img src="https://latex.codecogs.com/svg.image?\text{AUROC}=\int_{0}^{1}\text{TPR(FPR)},\d(\text{FPR})" alt="AUROC formula" /> </p>AUPR: Area Under the Precision-Recall Curve
<p align="center"> <img src="https://latex.codecogs.com/svg.image?\text{AUPR}=\int_{0}^{1}P(R),\d(\text{R})" alt="AUPR formula" /> </p>AP: Average Precision
<p align="center"> <img src="https://latex.codecogs.com/svg.image?\text{AP}=\sum_{n}(R_n-R_{n-1})P_n" alt="AP formula" /> </p>PRO: Per-Region Overlap is defined as the average relative overlap of the binary prediction P with each connected component Ck of the ground truth.
<p align="center"> <img src="https://latex.codecogs.com/svg.image?\text{PRO}=\frac{1}{K}\sum_{k=1}^{K}\frac{|P\cap&space;C_k|}{|C_k|}" alt="PRO formula" /> </p>F1max: F1-score-max (F1-max) -- F1-score at optimal threshold θ for a clearer view against potential data imbalance
<p align="center"> <img src="https://latex.codecogs.com/svg.image?\text{F1}_{\text{max}}(\theta)=\max_{\theta}\left(\frac{2&space;\times&space;\text{Precision}(\theta)&space;\times&space;\text{Recall}(\theta)}{\text{Precision}(\theta)+\text{Recall}(\theta)}\right)" alt="F1max formula" /> </p>3D Anomaly Detection Continue......
References
@article{bergmann2021mvtec,
title={The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization},
author={Bergmann, Paul and Jin, Xin and Sattlegger, David and Steger, Carsten},
journal={arXiv preprint arXiv:2112.09045},
year={2021}
}
@inproceedings{zou2022spot,
title={Spot-the-difference self-supervised pre-training for anomaly detection and segmentation},
author={Zou, Yang and Jeong, Jongheon and Pemula, Latha and Zhang, Dongqing and Dabeer, Onkar},
booktitle={European Conference on Computer Vision},
pages={392--408},
year={2022},
organization={Springer}
}