| ## Dataset Description | |
| NINCO (Natural In-Distribution Corruptions) is a dataset designed for evaluating robustness and OOD detection under natural corruptions. | |
| It includes real-world corrupted versions of ImageNet images to test model generalization beyond synthetic distortions. This split is derived from the **OpenOOD benchmark** OOD evaluation splits. | |
| - **Homepage:** https://github.com/j-cb/NINCO | |
| - **OpenOOD Benchmark:** https://github.com/Jingkang50/OpenOOD/ | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{bitterwolf2023or, | |
| title={In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation}, | |
| author={Bitterwolf, Julian and M{\"u}ller, Maximilian and Hein, Matthias}, | |
| booktitle={International Conference on Machine Learning}, | |
| pages={2471--2506}, | |
| year={2023}, | |
| organization={PMLR} | |
| } | |
| @inproceedings{yang2022openood, | |
| title={OpenOOD: Benchmarking Generalized Out-of-Distribution Detection}, | |
| author={Yang, Jingkang and Zhu, Zixuan and Liu, Yiyou and Song, Yiyou and Zhang, Yixuan and Pang, Tianyu and Chen, Shuqi and Wang, Hang and Lin, Min and Su, Hang and Zhu, Jun and Chen, Weiming and Zhang, Zheng}, | |
| booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, | |
| year={2022} | |
| } |