Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
Paper • 2505.10996 • Published • 1
Dataset Viewer
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 M2AD@2dc1be962f443a725ac25174bfbddecb0ed2b349
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
example_data = self.str2int(example_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
output = [self._strval2int(value) for value in values]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label M2AD@2dc1be962f443a725ac25174bfbddecb0ed2b349Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
📚 Paper • 🏠 Homepage
by Yunkang Cao*, Yuqi Cheng*, Xiaohao Xu, Yiheng Zhang, Yihan Sun, Yuxiang Tan, Yuxin Zhang, Weiming Shen,
🚀 Updates
We're committed to open science! Here's our progress:
- 2025/05/19: 📄 Paper released on ArXiv.
- 2025/05/16: 🌐 Dataset homepage launched.
- 2025/05/24: 🧪 Code release for benchmark evaluation! code
📊 Introduction
Visual Anomaly Detection (VAD) systems often fail in the real world due to sensitivity to viewpoint-illumination interplay—complex interactions that distort defect visibility. Existing benchmarks overlook this challenge.
Introducing M2AD (Multi-View Multi-Illumination Anomaly Detection), a large-scale benchmark designed to rigorously test VAD robustness under these conditions:
- 119,880 high-resolution images across 10 categories, 999 specimens, 12 views, and 10 illuminations (120 configurations).
- Two evaluation protocols:
- 🔄 M2AD-Synergy: Tests multi-configuration information fusion.
- 🧪 M2AD-Invariant: Measures single-image robustness to view-illumination variations.
- Key finding: SOTA VAD methods struggle significantly on M2AD, highlighting the critical need for robust solutions.
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