# Rad Robustness Eval

> Evaluates the robustness of image anomaly detection models against real-world imaging distortions, including free viewpoints, uneven illumination, and motion blur. It measures how well unsupervised and zero-shot methods localize and classify anomalies on industrial work platforms with foreign objects. Use when the user wants to benchmark on RAD, or asks about evaluating this task. Reports AUROC.

- Skill: `qhjqhj00/rad-robustness-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/rad-robustness-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/rad-robustness-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/rad-robustness-eval

---


# rad-robustness-eval

> RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection — Cheng et al. (2024) (arXiv:2406.07176, 2024)

## What this evaluates

Evaluates the robustness of image anomaly detection models against real-world imaging distortions, including free viewpoints, uneven illumination, and motion blur. It measures how well unsupervised and zero-shot methods localize and classify anomalies on industrial work platforms with foreign objects.

## Datasets

- **RAD** — total ?; splits: test (-1); repo https://github.com/hustCYQ/RAD-dataset

## Metrics

- `AUROC` **(primary)** — range: percent
  - Area Under the Receiver Operating Characteristic curve. Measures the trade-off between true positive rate and false positive rate across all classification thresholds.
- `Max-F1` — range: percent
  - Maximal F1 score. The highest F1 score achieved across all possible classification thresholds on the predicted anomaly scores.
- `AP` — range: percent
  - Average Precision. The area under the Precision-Recall curve, summarizing model performance across all thresholds.
- `AUPRO` — range: percent
  - Area Under Per Region Overlap curve. Evaluates pixel-level localization by computing the overlap ratio between predicted and ground truth anomaly masks for each image, then averaging across the dataset.

## Input / output format

**Input**: Resized 224×224 RGB images of industrial work platforms containing foreign objects (bolts, cable ties, sponges, tapes) under varying conditions (normal, uneven illumination, blurry, free views).

**Output**: Image-level anomaly score/prediction and pixel-wise anomaly score map or binary mask.

## Scoring recipe

```python
# Image-level
auroc = roc_auc_score(y_true_img, y_score_img)
ap = average_precision_score(y_true_img, y_score_img)
max_f1 = max(f1_score(y_true_img, (y_score_img > t)) for t in np.linspace(0, 1, 100))
# Pixel-level
aupro = compute_aupro(y_true_masks, y_score_maps) # AUPRO averages per-image overlap ratios
```

## Common pitfalls

- AUPRO is exclusively for pixel-level evaluation and should not be reported for image-level results.
- All input images are resized to 224×224, which may degrade pixel-level localization precision compared to original high-resolution captures.
- Zero-shot methods (WinCLIP, APRIL-GAN, SAA) are evaluated without any training on the RAD dataset, relying entirely on pre-trained foundation models.

## Evidence (verbatim from paper)

> Following the mainstream setting as in MVTec-AD, we employ several key metrics to evaluate the performance of anomaly detection methods, namely the Area Under the Receiver Operating Characteristic curve (AUROC), Maximal F1 score (Max-F1), Average Precision (AP), and Area Under Per Region Overlap (AUPRO). The first three indicators (AUROC, Max-F1, AP) are utilized for both image-level and pixel-level anomaly detection evaluation, and AUPRO is exclusively employed for pixel-level anomaly detection evaluation.

## Citation

```bibtex
@misc{cheng2024rad,
  title={RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection},
  author={Cheng et al. (2024)},
  year={2024},
  note={arXiv:2406.07176}
}
```

- arXiv: 2406.07176