README.md

---
license: mit
task_categories:
- image-classification
- image-segmentation
tags:
- anomaly-detection
- cold-start
---

# ArcAD Cold-Start Data Splits

Cold-start supervised data splits (JSON manifests) for **MVTec-AD, VisA, Real-IAD, and MANTA**, used by [ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection](https://huggingface.co/papers/2607.02252) (ECCV 2026).

- **Repository:** https://github.com/LGC-AD/ArcAD
- **Paper:** https://huggingface.co/papers/2607.02252

All paths use each dataset's **original download structure** — download the official datasets and the paths resolve directly. See the [ArcAD repository](https://github.com/LGC-AD/ArcAD) for usage.

### Split JSON format

Every `<category>.json` has the same schema:

```json
{
  "meta":   { "dataset": "mvtec", "category": "bottle", "num_labeled": 69, "num_test": 223 },
  "labeled":[ { "image": "bottle/train/good/000.png",            "mask": "",                                              "label": 0, "anomaly_class": "good" },
              { "image": "bottle/test/broken_large/005.png",     "mask": "bottle/ground_truth/broken_large/005_mask.png", "label": 1, "anomaly_class": "broken_large" } ],
  "test":   [ ... ]
}
```

- All paths are **relative to the dataset root** (the `--data_path` argument) and use each dataset's **original download layout**.
- `mask` is `""` for normal samples (no mask file).
- `label`: `0` = normal, `1` = anomaly.
- `anomaly_class`: `"good"` for normals; the defect sub-folder name (e.g. `broken_large`) for MVTec, `"anomaly"` for VisA / Real-IAD / MANTA.

The total number of labeled samples matches the cold-start protocol (e.g. MVTec-AD: 1089 normals + 121 anomalies; Real-IAD: 10940 normals + 1216 anomalies).

### Expected on-disk layout

The JSON paths resolve against the **official download structure** of each dataset. Point `--data_path` at the root shown below:

#### MVTec-AD
It contains over 5000 high-resolution images divided into fifteen different object and texture categories.
```
<data_path>/bottle/
    train/good/*.png
    test/good/*.png
    test/<defect_type>/*.png            # e.g. broken_large, broken_small, contamination, ...
    ground_truth/<defect_type>/<name>_mask.png
```

#### VisA
It contains 12 subsets corresponding to 12 different objects. There are 10,821 images with 9,621 normal and 1,200 anomalous samples.
```
<data_path>/candle/
    Data/Images/Normal/*.JPG
    Data/Images/Anomaly/*.JPG
    Data/Masks/Anomaly/*.png
```

#### Real-IAD
A large-scale challenging industrial AD dataset, containing 30 classes with totally 151,050 images.
```
<data_path>/realiad_1024/<category>/<image>      # image_path from realiad_jsons/sup/<cat>.json
<data_path>/realiad_jsons/sup/<category>.json    # authoritative labeled/test split
```

#### MANTA
It contains 38 categories and over 130K object-level images.
```
<data_path>/MANTA_TINY_256_cropped/<category>/<image>
<data_path>/sup_cropped/<category>.json          # authoritative labeled/test split
```

## Citation

If you find this work useful, please cite:

```bibtex
@article{han2026arcad,
  title   = {ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection},
  author  = {Han, Ningning and Fan, Lei and Guo, Jia and Cao, Yunkang and Su, Xiu and Cao, Feng and Di, Donglin and Su, Tonghua},
  journal = {arXiv preprint arXiv:2607.02252},
  year    = {2026}
}
```