AnomalyCLIP

https://github.com/zqhang/anomalyclip

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AnomalyCLIP introduces a novel approach to adapt CLIP for accurate zero-shot anomaly detection,...

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License:MIT

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### Run AnomalyCLIP

Source: https://github.com/zqhang/anomalyclip/blob/main/README.md

Commands to execute testing with pre-trained weights or to initiate training.

```bash
bash test.sh
```

```bash
bash train.sh
```

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### Generate SDD Dataset JSON

Source: https://github.com/zqhang/anomalyclip/blob/main/README.md

Run the SDD dataset generation script after navigating to the appropriate directory.

```bash
cd generate_dataset_json
python SDD.py
```

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### Generate MVTec Dataset JSON

Source: https://github.com/zqhang/anomalyclip/blob/main/README.md

Run the MVTec dataset generation script after navigating to the appropriate directory.

```bash
cd generate_dataset_json
python mvtec.py
```

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### BibTex Citation for AnomalyCLIP

Source: https://github.com/zqhang/anomalyclip/blob/main/README.md

Use this BibTex entry to cite the AnomalyCLIP paper in academic publications.

```bibtex
@inproceedings{zhou2023anomalyclip,
  title={AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection},
  author={Zhou, Qihang and Pang, Guansong and Tian, Yu and He, Shibo and Chen, Jiming},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2023}
}
```

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