README.md

metadata

license: mit
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification
pretty_name: MiniImageNet-C
size_categories:
  - 100K<n<1M
tags:
  - computer-vision
  - robustness
  - corruption
  - imagenet
  - benchmark
configs:
  - config_name: default
    data_files:
      - split: test
        path: '*/severity_5/*/*.JPEG'
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: class_label
      names:
        _type: Value
        dtype: string
    - name: corruption_type
      dtype: string
    - name: severity
      dtype: int32
  splits:
    - name: test
      num_bytes: 750000000
      num_examples: 750000
  download_size: 750000000
  dataset_size: 750000000

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.