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Models/EfficientAD-M

EfficientAD-M

Reported on 9 benchmarks across 1 task · 1 paper · 3 SOTA

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

Methodology9 results

  • Anomaly DetectiononMVTec LOCO AD
    Avg. Detection AUROC· 2023-03-25
    90.7
    best: 95.3 (CSAD)
    SOTA
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononMVTec LOCO AD
    Detection AUROC (only structural)· 2023-03-25
    94.7
    best: 95.9 (PUAD-M)
    SOTA
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononMVTec LOCO AD
    Segmentation AU-sPRO (until FPR 5%)· 2023-03-25
    79.8
    best: 83.2 (SAM-LAD)
    SOTA
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononMVTec AD
    Detection AUROC· 2023-03-25
    99.1
    best: 99.9 (GLASS)
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononMVTec AD
    FPS· 2023-03-25
    269
    best: 1016 (CPR-faster(TensorRT))
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononMVTec AD
    Segmentation AUPRO· 2023-03-25
    93.5
    best: 98.4 (WeakREST-Block)
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononVisA
    Detection AUROC· 2023-03-25
    98.1
    best: 99.8 (UniNet)
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononVisA
    Segmentation AUPRO (until 30% FPR)· 2023-03-25
    94
    best: 96.1 (AnomalyDINO-S (full-shot))
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535
  • Anomaly DetectiononMVTec LOCO AD
    Detection AUROC (only logical)· 2023-03-25
    86.8
    best: 98.1 (PSAD)
    EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesarXiv:2303.14535