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Anomaly Detection On Btad

Metrics

Detection AUROC
Segmentation AUPRO

Results

Performance results of various models on this benchmark

Model Name
Detection AUROC
Segmentation AUPRO
Paper TitleRepository
ReConPatch WRN-5095.897.5ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection-
PatchSVDD--Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation-
CPR94.885.1Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval-
MuSc (zero-shot)96.1683.43MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images-
RealNet96.1-RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection-
FastFlow+AltUB--AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection-
PNI--PNI : Industrial Anomaly Detection using Position and Neighborhood Information-
URD93.978.5Unlocking the Potential of Reverse Distillation for Anomaly Detection-
AD-CLSCNFs95.9372.77Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification
WeakREST-Un94.484.9Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers-
VT-ADL--VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization-
D3AD95.283.2Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection-
Reverse Distillation ++95.63-Revisiting Reverse Distillation for Anomaly Detection
PyramidFlow (Res18)95.8-PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow-
0 of 14 row(s) selected.
Anomaly Detection On Btad | SOTA | HyperAI