Papers › In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Julian Bitterwolf, Maximilian Müller, Matthias Hein
Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to the in-distribution task. The OOD detection performance when the in-distribution (ID) is ImageNet-1K is commonly being tested on a small range of test OOD datasets. We find that most of the currently used test OOD datasets, including datasets from the open set recognition (OSR) literature, have severe issues: In some cases more than 50% of the dataset contains objects belonging to one of the ID classes. These erroneous samples heavily distort the evaluation of OOD detectors. As a solution, we introduce with NINCO a novel test OOD dataset, each sample checked to be ID free, which with its fine-grained range of OOD classes allows for a detailed analysis of an OOD detector's strengths and failure modes, particularly when paired with a number of synthetic "OOD unit-tests". We provide detailed evaluations across a large set of architectures and OOD detection methods on NINCO and the unit-tests, revealing new insights about model weaknesses and the effects of pretraining on OOD detection performance. We provide code and data at https://github.com/j-cb/NINCO.
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Code
Syntology Ran 1 of 19 code samples harvested from 2 repositories linked to this paper; 18 have no recorded run. Of those that ran: 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
19 samples harvested; 1 ran; 0 honoured the contract we drafted; 18 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Out-of-Distribution Detection | ImageNet-1k vs NINCO | ViT-B-384 Mahalanobis (pre-trained on IN-21k) | AUROC | 95.0 | #2 of 5 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs NINCO | ViT-B-384 Mahalanobis (pre-trained on IN-21k) | FPR@95 | 27.5 | #2 of 5 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs NINCO | EffNetv2-M Relative Mahalanobis | AUROC | 88.9 | #4 of 5 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs NINCO | EffNetv2-M Relative Mahalanobis | FPR@95 | 54.9 | #4 of 5 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs NINCO | EffNetb7 Relative Cosine Sim | AUROC | 87.9 | #5 of 5 | Archive leaderboard | report |
| Out-of-Distribution Detection | ImageNet-1k vs NINCO | EffNetb7 Relative Cosine Sim | FPR@95 | 53.7 | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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