Papers › Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Jia Guo, Shuai Lu, Weihang Zhang, Fang Chen, Hongen Liao, Huiqi Li
Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behind state-of-the-art class-separated models. Our research aims to bridge this substantial performance gap. In this paper, we introduce a minimalistic reconstruction-based anomaly detection framework, namely Dinomaly, which leverages pure Transformer architectures without relying on complex designs, additional modules, or specialized tricks. Given this powerful framework consisted of only Attentions and MLPs, we found four simple components that are essential to multi-class anomaly detection: (1) Foundation Transformers that extracts universal and discriminative features, (2) Noisy Bottleneck where pre-existing Dropouts do all the noise injection tricks, (3) Linear Attention that naturally cannot focus, and (4) Loose Reconstruction that does not force layer-to-layer and point-by-point reconstruction. Extensive experiments are conducted across popular anomaly detection benchmarks including MVTec-AD, VisA, and Real-IAD. Our proposed Dinomaly achieves impressive image-level AUROC of 99.6%, 98.7%, and 89.3% on the three datasets respectively, which is not only superior to state-of-the-art multi-class UAD methods, but also achieves the most advanced class-separated UAD records.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MPDD | Dinomaly | Detection AUROC | 97.2 | #7 of 16 | Archive leaderboard | report |
| Anomaly Detection | MPDD | Dinomaly | Segmentation AUROC | 99.1 | #7 of 16 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-L (model-unified multi-class) | Detection AUROC | 99.77 | #8 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-L (model-unified multi-class) | Segmentation AP | 70.53 | #8 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-L (model-unified multi-class) | Segmentation AUPRO | 95.09 | #8 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-L (model-unified multi-class) | Segmentation AUROC | 98.54 | #8 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-B (model-unified multi-class) | Detection AUROC | 99.60 | #19 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-B (model-unified multi-class) | Segmentation AP | 69.29 | #19 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-B (model-unified multi-class) | Segmentation AUPRO | 94.79 | #19 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Dinomaly ViT-B (model-unified multi-class) | Segmentation AUROC | 98.35 | #19 of 148 | Archive leaderboard | report |
| Anomaly Detection | VisA | Dinomaly ViT-L (model-unified multi-class) | Detection AUROC | 98.9 | #4 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | Dinomaly ViT-L (model-unified multi-class) | F1-Score | 96.1 | #4 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | Dinomaly ViT-L (model-unified multi-class) | Segmentation AUPRO | 94.8 | #4 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | Dinomaly ViT-L (model-unified multi-class) | Segmentation AUPRO (until 30% FPR) | 94.8 | #4 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | Dinomaly ViT-L (model-unified multi-class) | Segmentation AUROC | 99.1 | #4 of 50 | Archive leaderboard | report |
| Multi-class Anomaly Detection | MVTec AD | Dinomaly-Large | Detection AUROC | 99.8 | #2 of 13 | Archive leaderboard | report |
| Multi-class Anomaly Detection | MVTec AD | Dinomaly-Large | Segmentation AUROC | 98.5 | #2 of 13 | Archive leaderboard | report |
| Multi-class Anomaly Detection | MVTec AD | Dinomaly-Base | Detection AUROC | 99.6 | #4 of 13 | Archive leaderboard | report |
| Multi-class Anomaly Detection | MVTec AD | Dinomaly-Base | Segmentation AUROC | 98.4 | #4 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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