Vision / Benchmark
Industrial anomaly detection
Industrial anomaly detection datasets; grouping used on this site, not an official suite.
Test split: Standard (unsupervised / 1-class) protocol: models are trained on the defect-free train split of each category and evaluated on the test split, which contains normal and anomalous images with pixel masks (MVTec AD; VisA 1-class: 90% of normal images train, 10% normal + all anomalous test; Real-IAD UIAD). MVTec AD 2 test ground truth is partly private (evaluation server). Zero-shot protocol: no training samples of the target dataset are used (WinCLIP uses CLIP 'without further tuning'); methods that learn prompts/adapters use auxiliary data, and the common cross-dataset protocol trains on the TEST split of another dataset - e.g. AnomalyCLIP fine-tunes on MVTec AD test data to evaluate on all other datasets, and on VisA test data to evaluate on MVTec AD. KolektorSDD2's official train split also contains defective images.
Contamination matrix
- TestTest data seen18
- SeenSeen, split unclear7
- TrainTraining data seen2
- Related data seen0
- NoNot seen33
- ?Unknown236
Hover a cell for the reason; click it for the evidence.
Notes
Grouping used on this site, not an official suite. MVTec AD 2 vs MVTec AD: separate datasets from the same group; no image re-use stated.
Sources
- MVTec AD (CVPR 2019): train normal-only, test normal + defective · paper · retrieved 2026-10-15
- VisA paper: 1-class and 2-class protocols · paper · retrieved 2026-10-15
- Real-IAD paper: UIAD and FUIAD protocols · paper · retrieved 2026-10-15
- MVTec AD 2 paper: public/private test sets · paper · retrieved 2026-10-15
- WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation · paper · retrieved 2026-10-15
- AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection (ICLR 2024) · paper · retrieved 2026-10-15