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🐉 News

💡 Property


🛠️ Getting Started

Installation

Dataset Preparation

Please refer to Datasets Description for preparing visual AD datasets as needed.

Train (Multi-class Unsupervised AD setting by default, MUAD)

Test

Visualization

How to Build a Custom Approach

  1. Add a model config cfg_model_MODEL_NAME to configs/__base__
  2. Add configs to configs/MODEL_NAME/CFG.py for training and testing.
  3. Add a model implementation file model/MODEL_NAME.py
  4. Add a trainer implementation file trainer/MODEL_NAME_trainer.py
  5. (Optional) Add specific files to data, loss, optim, etc.

📜 MUAD Results on Popular AD Datasets

Detailed results are available on the benchmark page

Citation

If you use this toolbox or benchmark in your research, please cite our related works.

@article{ader,
  title={ADer: A Comprehensive Benchmark for Multi-class Visual Anomaly Detection},
  author={Jiangning Zhang and Haoyang He and Zhenye Gan and Qingdong He and Yuxuan Cai and Zhucun Xue and Yabiao Wang and Chengjie Wang and Lei Xie and Yong Liu},
  journal={arXiv preprint arXiv:2406.03262},
  year={2024}
}

@inproceedings{realiad,
  title={Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection},
  author={Wang, Chengjie and Zhu, Wenbing and Gao, Bin-Bin and Gan, Zhenye and Zhang, Jianning and Gu, Zhihao and Qian, Shuguang and Chen, Mingang and Ma, Lizhuang},
  booktitle={CVPR},
  year={2024}
}

@article{vitad,
  title={Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection},
  author={Zhang, Jiangning and Chen, Xuhai and Wang, Yabiao and Wang, Chengjie and Liu, Yong and Li, Xiangtai and Yang, Ming-Hsuan and Tao, Dacheng},
  journal={arXiv preprint arXiv:2312.07495},
  year={2023}
}

@article{invad,
  title={Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark},
  author={Jiangning Zhang and Chengjie Wang and Xiangtai Li and Guanzhong Tian and Zhucun Xue and Yong Liu and Guansong Pang and Dacheng Tao},
  journal={arXiv preprint arXiv:2404.10760},
  year={2024}
}

@article{mambaad,
  title={MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection},
  author={He, Haoyang and Bai, Yuhu and Zhang, Jiangning and He, Qingdong and Chen, Hongxu and Gan, Zhenye and Wang, Chengjie and Li, Xiangtai and Tian, Guanzhong and Xie, Lei},
  year={2024}
}