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Anomaly Classification On Goodsad

评估指标

AUPR
AUROC

评测结果

各个模型在此基准测试上的表现结果

模型名称
AUPR
AUROC
Paper TitleRepository
PatchCore-100%86.185.5Towards Total Recall in Industrial Anomaly Detection-
PatchCore-1%83.381.4Towards Total Recall in Industrial Anomaly Detection-
SimpleNet78.775.3SimpleNet: A Simple Network for Image Anomaly Detection and Localization-
RD4AD68.266.5Anomaly Detection via Reverse Distillation from One-Class Embedding-
DRAEM7165.9DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection-
SPADE68.764.1Sub-Image Anomaly Detection with Deep Pyramid Correspondences-
NSA71.867.3Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization-
CFLOW-AD75.371.2CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows-
MiniMaxAD-fr-86.1MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection-
CutPaste62.860.2CutPaste: Self-Supervised Learning for Anomaly Detection and Localization-
f-AnoGAN66.662.8f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks
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