Anomaly Detection On Mvtec Loco Ad

평가 지표

Avg. Detection AUROC

Detection AUROC (only logical)

Detection AUROC (only structural)

Segmentation AU-sPRO (until FPR 5%)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

					Paper Title	Repository
CSAD	95.3	96.7	94.0	-	CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection
PSAD	94.9	98.1	91.6	-	Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
PUAD-M	94.4	93.7	95.9	-	PUAD: Frustratingly Simple Method for Robust Anomaly Detection
SINBAD+EfficientAD	94.2	95.8	94.2	-	Set Features for Anomaly Detection
PUAD-S	93.1	92.0	94.1	-	PUAD: Frustratingly Simple Method for Robust Anomaly Detection
SAM-LAD	90.7	-	-	83.2	SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection	-
EfficientAD-M	90.7	86.8	94.7	79.8	EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
SLSG	90.3	89.6	91.4	67.3	SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification	-
ComAD+PatchCore	90.1	89.4	90.9	-	Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
EfficientAD-S	90.0	85.8	94.1	77.8	EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
ComAD+AST	89.8	90.1	89.4	-	Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
SINBAD Ens	88.3	91.2	85.5	-	Set Features for Anomaly Detection
ComAD+RD4AD	88.2	87.5	88.8	-	Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
HETMM	88.1	83.2	92.9	-	Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection
ComAD+DRAEM	87.9	85.9	89.9	-	Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
GRAD	87.5	-	-	-	Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection	-
SINBAD	86.8	88.9	84.7	-	Set Features for Fine-grained Anomaly Detection
LADMIM	86.0	83.1	90.3	-	LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space	-
THFR	86.0	85.2	86.7	74.1	Template-guided Hierarchical Feature Restoration for Anomaly Detection	-
DSKD	84.0	81.2	86.9	73.0	Contextual Affinity Distillation for Image Anomaly Detection	-

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Anomaly Detection On Mvtec Loco Ad

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