Error code:   ConfigNamesError
Exception:    ValueError
Message:      Please provide either num_classes, names or names_file.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
                  return HubDatasetModuleFactory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 605, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2027, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1872, in from_dict
                  obj = generate_from_dict(dic)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in <dictcomp>
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1478, in generate_from_dict
                  return class_type(**{k: v for k, v in obj.items() if k in field_names})
                File "<string>", line 5, in __init__
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1020, in __post_init__
                  raise ValueError("Please provide either num_classes, names or names_file.")
              ValueError: Please provide either num_classes, names or names_file.

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MiniImageNet-C Dataset

Dataset Description

MiniImageNet-C is a compact version of the ImageNet-C robustness benchmark dataset. It contains corrupted images from ImageNet designed to test the robustness of computer vision models to various types of image corruptions.

Dataset Summary

This dataset is a subset of the original ImageNet-C dataset, containing:

15 corruption types: gaussian_noise, shot_noise, impulse_noise, defocus_blur, glass_blur, motion_blur, zoom_blur, snow, frost, fog, brightness, contrast, elastic_transform, pixelate, jpeg_compression
1 severity level: Only severity level 5 (most severe)
50 images per class per corruption: Randomly selected from the original dataset
1000 classes: All ImageNet classes
Total images: 750,000 (15 corruptions × 50 images × 1000 classes)

The dataset uses a fixed random seed (7600) for reproducible image selection.

Supported Tasks and Leaderboards

Image Classification: Multi-class image classification with 1000 classes
Robustness Evaluation: Testing model performance under various image corruptions
Benchmarking: Comparing model robustness across different corruption types

Languages

Not applicable (computer vision dataset).

Dataset Structure

Data Instances

Each instance contains:

image: A PIL Image object
label: Integer class label (0-999)
corruption_type: String indicating the type of corruption applied
severity: Integer indicating corruption severity (always 5)

Data Fields

image (PIL Image): The corrupted image
label (int): Class label corresponding to ImageNet classes
corruption_type (string): One of 15 corruption types
severity (int): Corruption severity level (always 5)

Data Splits

The dataset contains only a test split with 750,000 images total.

Dataset Creation

Curation Rationale

This dataset was created to provide a more manageable subset of ImageNet-C for:

Quick robustness evaluation during development
Reduced computational requirements for benchmarking
Educational purposes and prototyping

Source Data

Initial Data Collection and Normalization

The source data comes from ImageNet-C, which applies algorithmic corruptions to the original ImageNet validation set.

Who are the source language producers?

Not applicable.

Annotations

Annotation process

Labels are inherited from the original ImageNet dataset.

Who are the annotators?

Original ImageNet annotators.

Personal and Sensitive Information

The dataset contains no personal or sensitive information.

Considerations for Using the Data

Social Impact of Dataset

This dataset is intended for research purposes to improve the robustness of computer vision models.

Discussion of Biases

Inherits any biases present in the original ImageNet dataset.

Other Known Limitations

Limited to severity level 5 only
Reduced number of images per class may not capture full diversity
May not be representative of real-world corruptions

Additional Information

Dataset Curators

Created for research purposes based on ImageNet-C.

Licensing Information

MIT License

Citation Information

@article{hendrycks2019robustness,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Dan Hendrycks and Thomas Dietterich},
  journal={International Conference on Learning Representations},
  year={2019}
}

Contributions

This compact version was created to provide an accessible subset of ImageNet-C for rapid prototyping and development.

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