Official PyTorch implementation of “PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation” (CVPR 2021).
Use the following commands:
cd path-to-PANDA-directory
virtualenv venv --python python3
source venv/bin/activate
pip install -r requirements.txt --find-links https://download.pytorch.org/whl/torch_stable.html
Use the following commands:
cd path-to-PANDA-directory
mkdir data
Download:
Extract these files into path-to-PANDA-directory/data and unzip tiny.zip
To replicate the results on CIFAR10, FMNIST for a specific normal class with EWC:
python panda.py --dataset=cifar10 --label=n --ewc --epochs=50
python panda.py --dataset=fashion --label=n --ewc --epochs=50
To replicate the results on CIFAR10, FMNIST for a specific normal class with early stopping:
python panda.py --dataset=cifar10 --label=n
python panda.py --dataset=fashion --label=n
Where n indicates the id of the normal class.
To run experiments on different datasets, please set the path in utils.py to the desired dataset.
To replicate the results on CIFAR10 for a specific normal class:
python outlier_exposure.py --dataset=cifar10 --label=n
Where n indicates the id of the normal class.
See our new paper “Mean-Shifted Contrastive Loss for Anomaly Detection” which achieves state-of-the-art anomaly detection performance on multiple benchmarks including 97.5% ROC-AUC on the CIFAR-10 dataset.
See our new paper “Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection” which achieves state-of-the-art video anomaly detection performance on multiple benchmarks including 85.9% ROC-AUC on the ShanghaiTech dataset.
If you find this useful, please cite our paper:
@inproceedings{reiss2021panda,
title={PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation},
author={Reiss, Tal and Cohen, Niv and Bergman, Liron and Hoshen, Yedid},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2806--2814},
year={2021}
}