Awesome

RTL

This repo is the official implementation of Test-Time Linear Out-of-Distribution Detection.

Environment

conda create -n RTL python=3.10
conda activate RTL
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip install scikit-learn pandas scikit-image

Datasets and checkpoints

For CIFAR, we use Textures, Places365, LSUN-C, LSUN-R, iSUN and SVHN as our OOD datasets. Please organize the datasets as follows:

├── dtd
│   └── images
├── iSUN
│   └── iSUN_patches
├── LSUN
│   └── test
├── LSUN_resize
│   └── LSUN_resize
├── Places365
│   └── test_256
└── SVHN
    └── test_32x32.mat

For ImageNet, we follow gradnorm_ood and use their dataset splits iNaturalist, SUN, Places and Textures. For fair comparision, we use their dataset splits of iNaturalist, Places and SUN and their checkpoint. Please organize the datasets as follows:

├── dtd
│   ├── images
├── iNaturalist
│   └── images
├── Places
│   └── images
└──  SUN
    └── images

Evaluation

For CIFAR

cd CIFAR/CIFAR/
python RTL.py/RTL_plus.py --method_name cifar10_wrn_pretrained/cifar100_wrn_pretrained --score MSP/energy/xent --exp_num 0 --alpha 1e-5 --T 1 --num_to_avg 10
python RTL.py/RTL_plus.py --method_name cifar10_wrn_pretrained/cifar100_wrn_pretrained --score Odin --exp_num 0 --alpha 1e-5 --T 1 --noise  0.0024 --num_to_avg 10

For ImageNet

cd ImageNet
python feature_extraction.py --in_datadir link_to_imagenet1k_val --out_datadir link_to_ood_datasets --model BiT-S-R101x1 --model_path checkpoints/BiT-S-R101x1-flat-finetune.pth.tar --batch 32
python RTL.py/RTL_plus.py --score MSP/energy/ODIN/xent --alpha 1e-7 --reduce_method pca --reduce_dim 32

Citation

If you find our paper useful for your research and applications, please cite using this BibTeX:

@InProceedings{Fan_2024_CVPR,
    author    = {Fan, Ke and Liu, Tong and Qiu, Xingyu and Wang, Yikai and Huai, Lian and Shangguan, Zeyu and Gou, Shuang and Liu, Fengjian and Fu, Yuqian and Fu, Yanwei and Jiang, Xingqun},
    title     = {Test-Time Linear Out-of-Distribution Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {23752-23761}
}