Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection (CVPR 2023)
SCOOD benchmarks download link:
The codebase accesses the SCOOD benchmarks from the root directory in a folder named data/
by default, i.e.
├── ...
├── data
│ ├── images
│ └── imglist
├── scood
├── test.py
├── train.py
├── ...
- Python >= 3.8
- Pytorch = 1.8.1
- CUDA >= 11.3
- torchvision=0.9.1
- faiss-gpu=1.7.1
You can run the following script (specifying the output and data directories) which perform training & testing for CIFAR10/100 experimental results:
bash cifar10.sh output_dir data_dir
bash cifar100.sh output_dir data_dir
The information during training can be monitored in real-time in output_dir/log.txt
and the results will be saved in output_dir/results.csv
.
This paper follows the excellent work from SCOOD.
If our work is useful for your research, please consider citing our paper :
@inproceedings{lu2022etood,
title={Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection},
author={Fan Lu, Kai Zhu, Wei Zhai, Kecheng Zheng and Yang Cao},
booktitle={CVPR},
year={2023}
}