Revisiting Reverse Distillation for Anomaly Detection (CVPR 2023)

Official code of CVPR 2023 paper: Revisiting Reverse Distillation for Anomaly Detection.

    The paper proposes the RD++ approach for anomaly detection by enriching feature compactness and suppressing anomalous signals through a multi-task learning design. For the feature compactness task, RD++ introduces the self-supervised optimal transport method. For the anomalous signal suppression task, RD++ simulates pseudo-abnormal samples with simplex noise and minimizes the reconstruction loss.
    RD++ achieves a new state-of-the-art benchmark on the challenging MVTec dataset for both anomaly detection and localization. More importantly, when compared to recent SOTA methods, RD++ runs 6.x times faster than PatchCore and 2.x times faster than CFA, while introducing a negligible latency compared to RD.

Table of Contents

• Revisiting Reverse Distillation for Anomaly Detection (CVPR 2023)
• Libraries
• Data Preparations
• Train
• Evaluation
• Quick Experiments
• Citation
• Contact
• Acknowledgement

Libraries

- geomloss
- numba

or (preferably whithin a fresh env to avoid conflicts):

pip install -r requirements.txt

Data Preparations

Download MVTEC dataset from [Link]

Train

To train and test the RD++ method on 15 classes of MVTEC, for example, with two classes: carpet and leather, please run:

python main.py --save_folder RD++  \
               --classes carpet leather

Evaluation

If you only need to perform inference with checkpoints, please run:

python inference.py --checkpoint_folder RD++  \
                    --classes carpet leather

The pretrained weights can be found here [Google Drive]

Quick Experiments

Try the Colab here using

Citation

Please cite our paper if you find it's helpful in your work.

@InProceedings{Tien_2023_CVPR,
    author    = {Tien, Tran Dinh and Nguyen, Anh Tuan and Tran, Nguyen Hoang and Huy, Ta Duc and Duong, Soan T.M. and Nguyen, Chanh D. Tr. and Truong, Steven Q. H.},
    title     = {Revisiting Reverse Distillation for Anomaly Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {24511-24520}
}

Contact

If you have any questions, feel free to reach out to me at trandinhtienftu95@gmail.com (Tran Dinh Tien) or open an issue in this repository.

Acknowledgement

We use RD as the baseline. Also, we use the Simplex Noise. We are thankful to their brilliant works!