A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
<|reference_start|>A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection: Visual anomaly detection aims to identify anomalous regions in images through unsupervised learning paradigms, with increasing application demand and value in fields such as industrial inspection and medical lesion detection. Despite significant progress in recent years, there is a lack of comprehensive benchmarks to adequately evaluate the performance of various mainstream methods across different datasets under the practical multi-class setting. The absence of standardized experimental setups can lead to potential biases in training epochs, resolution, and metric results, resulting in erroneous conclusions. This paper addresses this issue by proposing a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework that is highly extensible for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. Additionally, we have proposed the GPU-assisted ADEval package to address the slow evaluation problem of metrics like time-consuming mAU-PRO on large-scale data, significantly reducing evaluation time by more than \textit{1000-fold}. Through extensive experimental results, we objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection. We hope that ADer will become a valuable resource for researchers and practitioners in the field, promoting the development of more robust and generalizable anomaly detection systems. Full codes are open-sourced at https://github.com/zhangzjn/ader.<|reference_end|>
@article{zhang2024a,
    title={A Comprehensive Library for Benchmarking Multi-class Visual Anomaly
  Detection},
    author={Jiangning Zhang, Haoyang He, Zhenye Gan, Qingdong He, Yuxuan Cai,
  Zhucun Xue, Yabiao Wang, Chengjie Wang, Lei Xie, Yong Liu},
    journal={arXiv preprint arXiv:2406.03262},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03262},
    primaryClass={cs.CV}
}