zenodoopen

NINCO (Out-Of-Distribution detection dataset for ImageNet)

The NINCO (No ImageNet Class Objects) dataset is introduced in the ICML 2023 paper <a href="https://arxiv.org/abs/2306.00826">In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation</a>. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K . The NINCO main dataset consists of 64 OOD classes with a total of 5879 samples. These OOD classes were selected to have no categorical overlap with any classes of ImageNet-1K. Each sample was inspected individually by the authors to not contain ID objects. Besides NINCO, included are (in the same .tar.gz file) truly OOD versions of 11 popular OOD datasets with in total 2715 OOD samples. Further included are 17 OOD unit-tests, with 400 samples each. Code for loading and evaluating on each of the three datasets is provided at <a href="https://github.com/j-cb/NINCO">https://github.com/j-cb/NINCO</a>.   When using NINCO, please consider citing (besides the bibtex given below) the following data sources that were used to create NINCO: <ul> <li>Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022.  </li> <li>Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014.  </li> <li>Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017.  </li> <li>Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021.  </li> <li>Li et al.: ”Caltech 101 (1.0)”, 2022.</li> <li>Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020.</li> <li>The iNaturalist project: https://www.inaturalist.org/</li> </ul> When using NINCO_popular_datasets_subsamples, additionally to the above, please consider citing: <ul> <li>Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014.  </li> <li>Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021.  </li> <li>Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022.  </li> <li>Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016.  </li> <li>Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022.  </li> <li>Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022.  </li> <li>Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023.  </li> </ul> For citing our paper, we would appreciate using the following bibtex entry (this will be updated once the ICML 2023 proceedings are public): @inproceedings{     bitterwolf2023ninco,     title={In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation},     author={Julian Bitterwolf and Maximilian Mueller and Matthias Hein},     booktitle={ICML},     year={2023},     url={https://proceedings.mlr.press/v202/bitterwolf23a.html} }

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 4
Access: 16
Reuse readiness: 8
Engagement: 0

Topics

out-of-distribution detection deep learning computer vision