HyperAI超神经

Anomaly Detection On Mpdd

评估指标

Detection AUROC

Segmentation AUROC

评测结果

各个模型在此基准测试上的表现结果

			Paper Title
GLASS	99.6	99.4	A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
DMDD	98.10	98.96	Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
PBAS	97.7	98.8	Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
GLAD	97.5	98.7	GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
POUTA	97.5	-	Produce Once, Utilize Twice for Anomaly Detection
Dinomaly	97.2	99.1	Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
RealNet	96.3	98.2	RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
DiffusionAD	96.2	98.5	DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
ULSAD	95.73	97.45	Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
LeMO	87.4	97.8	Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision
AdaCLIP	82.5	96.1	AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
FastRecon	82.5	97.9	FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
PatchCore	82.12	95.66	Towards Total Recall in Industrial Anomaly Detection
KAnoCLIP	77.8	98.3	KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
ADSPR	-	96.8	Anomaly Detection using Score-based Perturbation Resilience

0 of 15 row(s) selected.

HyperAI超神经

Anomaly Detection On Mpdd

评估指标

Detection AUROC

Segmentation AUROC

评测结果

各个模型在此基准测试上的表现结果

			Paper Title
GLASS	99.6	99.4	A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
DMDD	98.10	98.96	Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
PBAS	97.7	98.8	Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
GLAD	97.5	98.7	GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
POUTA	97.5	-	Produce Once, Utilize Twice for Anomaly Detection
Dinomaly	97.2	99.1	Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
RealNet	96.3	98.2	RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
DiffusionAD	96.2	98.5	DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
ULSAD	95.73	97.45	Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection
LeMO	87.4	97.8	Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision
AdaCLIP	82.5	96.1	AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
FastRecon	82.5	97.9	FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
PatchCore	82.12	95.66	Towards Total Recall in Industrial Anomaly Detection
KAnoCLIP	77.8	98.3	KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
ADSPR	-	96.8	Anomaly Detection using Score-based Perturbation Resilience

0 of 15 row(s) selected.