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Industrial anomaly detection methods and benchmark papers
Passed 22 of 22 test cases with the skill, and 16 of 22 without it.
and 9 more in the full report
6 fixed16 passed either way0 still failing0 broke
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One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am writing a survey paper on automated visual defect inspection for manufacturing. Could you provide a structured taxonomy of industrial anomaly detection methodologies, list prominent models within memory banks, normalizing flows, and foundation approaches with their benchmark performance, and explain the key evaluation metrics used for image and pixel level evaluation?
| Checked | Without skill | With skill |
|---|---|---|
| Include 3D Anomaly Detection as a taxonomy branch covering point clouds or multi-modal RGB and 3D data. | Missed | Passed |
| Report SimpleNet as a feature adaptation method achieving 99.6% MVTec AUROC performance. | Missed | Passed |
| Include RD4AD with 98.5% MVTec AUROC performance. | Missed | Passed |
| Include AnomalyGPT with 96.3% MVTec AUROC performance. | Missed | Passed |
The instructions your model reads when the skill loads.
Industrial anomaly detection uses machine learning to identify defects, faults, and anomalies in manufacturing and quality inspection. This curated collection covers methods from reconstruction-based (autoencoders) to memory-bank approaches (PatchCore), normalizing flows, knowledge distillation, and foundation model-based detectors. Includes benchmark datasets, evaluation metrics, and real-world deployment considerations.
Anomaly Detection Methods
├── Reconstruction-based
│ ├── Autoencoder (AE, VAE)
│ ├── GAN-based (AnoGAN, GANomaly)
│ └── Diffusion-based (AnoDDPM)
├── Embedding-based
│ ├── Memory bank (PatchCore, PaDiM)
│ ├── Knowledge distillation (STPM, RD4AD)
│ └── Self-supervised (CutPaste, DRAEM)
├── Normalizing Flows
│ ├── FastFlow, CFLOW-AD, CS-Flow
│ └── DifferNet
├── Foundation Models
│ ├── CLIP-based (WinCLIP, AnomalyCLIP)
│ ├── SAM-based (GroundedSAM-AD)
│ └── Vision-language (AnomalyGPT)
└── 3D Anomaly Detection
├── Point cloud methods
└── Multi-modal (RGB + 3D)
| Method | Year | Approach | MVTec AUROC |
|---|---|---|---|
| PatchCore | 2022 | Memory bank | 99.1% |
| PaDiM | 2021 | Multivariate Gaussian | 97.9% |
| RD4AD | 2022 | Knowledge distillation | 98.5% |
| FastFlow | 2022 | Normalizing flow | 99.4% |
| SimpleNet | 2023 | Feature adaptation | 99.6% |
| WinCLIP | 2023 | CLIP zero-shot | 95.2% |
test cases passed, vs 16 / 22 without
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