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Datasets:
zhouk777
/
RAD

Tasks:
Image Classification
Image Segmentation
Modalities:
3D
Size:
1K<n<10K
ArXiv:
Tags:
anomaly-detection
industrial-inspection
3d
multi-view
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label RAD@5eed55ab545461eb3173ab79e1a468cc0d0f8704
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label RAD@5eed55ab545461eb3173ab79e1a468cc0d0f8704

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RAD with pixel masks

Real-world multi-view Anomaly Detection dataset (RAD), released here with refined pixel-level ground-truth masks.

Files

File Size Contents
Anomaly_refine_msk.zip 201 MB RAD with pixel masks — includes ground_truth/ masks per anomaly type
Anomaly_refine_nonmsk.zip 5.85 GB RAD without masks — full-resolution image set, no ground_truth/

Structure 

Anomaly_refine_msk/
├── binderclip/
│   ├── train/good/                 # normal training views
│   ├── test/good/                  # normal test views
│   ├── test/{missing,scratched,…}/ # anomalous test views
│   ├── ground_truth/{missing,…}/   # pixel masks
│   └── transforms.json             # camera metadata
├── binderclip2/
└── …                               # 18 physical-instance directories

The paper reports 13 semantic categories: binder clip, bowl, box, can, charger, cup 1, cup 2, glue bottle, phone case, rubber duck, spoon, spray bottle, tennis ball. Multiple physical instances of binder clip, cup 2, glue bottle and phone case are kept in separate directories and aggregated into their semantic category for reporting.

Download 

pip install -U huggingface_hub

# just the masked split (201 MB)
hf download zhouk777/RAD Anomaly_refine_msk.zip --repo-type dataset --local-dir .
unzip Anomaly_refine_msk.zip

# the full unmasked split (5.85 GB)
hf download zhouk777/RAD Anomaly_refine_nonmsk.zip --repo-type dataset --local-dir .

Python:

from huggingface_hub import hf_hub_download

path = hf_hub_download("zhouk777/RAD", "Anomaly_refine_msk.zip", repo_type="dataset")

Validate an extracted archive with the release utilities:

rad-benchmark validate /path/to/Anomaly_refine_msk --require-poses

Citation 

@misc{zhou2024rad,
  title         = {RAD: A Realistic Multi-View Benchmark for Pose-Agnostic Anomaly Detection},
  author        = {Zhou, Kaichen and Chang, Xinhai and Kim, Taewhan and Zhang, Jiadong and Cao, Yang and Peng, Chufei and Zhan, Fangneng and Zhao, Hao and Dong, Hao and Ting, Kai Ming and Zhu, Ye},
  year          = {2024},
  eprint        = {2410.00713},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  doi           = {10.48550/arXiv.2410.00713},
  url           = {https://arxiv.org/abs/2410.00713}
}

The code license of the GitHub repository does not automatically grant rights to the dataset; consult the dataset authors for its applicable terms.
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