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Reconstruction by Inpainting for visual Anomaly Detection (RIAD) in PyTorch

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).

PipeLine

Prerequisites

• PyTorch 1.5
• sklearn, matplotlib
• kornia ( incompatible with PyTorch>=1.6.0 so far )
  The kornia package is used for its medianfilter function. You may find a substitution if you want to get rid of this dependency.

Visualization demo of randomly generated mosaic masks

Please check this mosaic.ipynb file

Usage

To train RIAD on MVTec AD dataset:

python train.py --obj zipper --data_path [your-mvtec_ad-data-path]

Then to test:

python test.py --obj zipper --data_path [your-mvtec_ad-data-path] --checkpoint_dir [your-saved-weights-path]

Finally, you will get results like img_ROCAUC (anomaly detection) around 0.97 and pixel_ROCAUC (anomaly segmetation) around 0.98

Localization results

References

Vitjan Zavrtanik, Matej Kristan, Danijel Skčaj,<br> Reconstruction by inpainting for visual anomaly detection,<br> Pattern Recognition,<br> 2020,<br> 107706,<br> ISSN 0031-3203<br>

Acknowledgement

Thanks for the paper authors.
A big thanks to xiahaifeng1995 for contributing most of the codes.