SuperSimpleNet

Reported on 5 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology5 results

Anomaly DetectiononMVTec AD
Detection AUROC· 2024-08-06
98.4
best: 99.9 (GLASS)
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection arXiv:2408.03143
Anomaly DetectiononMVTec AD
Segmentation AUPRO· 2024-08-06
91.1
best: 98.4 (WeakREST-Block)
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection arXiv:2408.03143
Anomaly DetectiononVisA
Detection AUROC· 2024-08-06
93.4
best: 99.8 (UniNet)
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection arXiv:2408.03143
Anomaly DetectiononVisA
Segmentation AUPRO· 2024-08-06
87.4
best: 96 (DiffusionAD)
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection arXiv:2408.03143
Anomaly DetectiononVisA
Segmentation AUPRO (until 30% FPR)· 2024-08-06
87.4
best: 96.1 (AnomalyDINO-S (full-shot))
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection arXiv:2408.03143