Anomaly Classification on MVTecAD
Accuracy (% )
- 2025-01-27 — Echo: Accuracy (% ) 72.9
- 2025-05-05 — VELM: Accuracy (% ) 81.4
| Rank | Model | Accuracy (% ) | Paper | Code | Year |
|---|---|---|---|---|---|
| 1 | VELM | 81.4 | Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models | sassanmtr/velm | 2025 |
| 2 | Echo | 72.9 | Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? | 2025 | |
| 3 | VELM | 81.4 | Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models | sassanmtr/velm | 2025 |
| 4 | Echo | 72.9 | Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? | 2025 | |
| 5 | VELM | 81.4 | Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models | sassanmtr/velm | 2025 |
| 6 | Echo | 72.9 | Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? | 2025 |