NeurIPS22 paper "RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection"

In-Distribution (ID) dataset. Please download ImageNet-1k and use its validation set as the ID set.

Out-of-Distribution (OOD) dataset. For the used OOD datasets (iNaturalist, SUN, Places, and Textures), please download them from the following links:

wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/iNaturalist.tar.gz
wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/SUN.tar.gz
wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/Places.tar.gz
wget https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz

For the large-scale Species dataset, please download the four subsets (Protozoa, Microorganisms, Plants, and Mollusks) from the official link.

For SqueezeNet, it is already available in the Pytorch library. For BiT-S ResNetv2-101 and T2T-ViT-24, one can download the BiT-S ResNetv2-101 and T2T-ViT by the following links:

wget http://pages.cs.wisc.edu/~huangrui/finetuned_model/BiT-S-R101x1-flat-finetune.pth.tar
wget https://github.com/yitu-opensource/T2T-ViT/releases/download/main/82.6_T2T_ViTt_24.pth.tar

For more BiT pre-trained models, one can also refer to BiT-S pre-trained families.

Check train_resnetv2.sh, train_squeezenet.sh, and train_t2tvit.sh in the scripts folder for the specific usages on each architectures.

Besides our proposed RankFeat, the previous post hoc methods are also implemented here, including MSP, ODIN, Energy, Mahalanobis, GradNorm, and ReAct.

If you think the code is helpful to your research, please consider citing our paper:

@inproceedings{song2022rankfeat,
  title={RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection},
  author={Song, Yue and Sebe, Nicu and Wang, Wei},
  booktitle={NeurIPS},
  year={2022}
}

The code is built based on MOS and GradNorm. If you have any questions or suggestions, please feel free to contact me via yue.song@unitn.it.