CIFAR-100-C

Overview

CIFAR-100-C is a corrupted version of the CIFAR-100 dataset designed for benchmarking neural network robustness to common corruptions and perturbations. It consists of the CIFAR-100 test set with 19 different types of corruptions applied at 5 severity levels each.

• Test: 950,000 images (19 corruptions × 5 severity levels × 10,000 images)

• Train: N/A (test-only dataset for robustness evaluation)

The dataset includes corruptions such as noise, blur, weather effects, and digital artifacts to evaluate how well models trained on clean CIFAR-100 data generalize to corrupted inputs.

Data Structure

When accessing an example using ds[i], you will receive a dictionary with the following keys:

Key	Type	Description
image	PIL.Image.Image	32×32×3 RGB corrupted image
label	int	Fine-grained class label (0-99)
corruption_name	str	Type of corruption applied (e.g., “gaussian_noise”, “fog”)
corruption_level	int	Severity level of corruption (1-5, where 5 is most severe)

Corruption Types

The dataset includes 19 corruption types across different categories:

Noise: - gaussian_noise - shot_noise - impulse_noise - speckle_noise

Blur: - defocus_blur - glass_blur - motion_blur - zoom_blur - gaussian_blur

Weather: - snow - frost - fog

Digital: - brightness - contrast - elastic_transform - pixelate - jpeg_compression - saturate - spatter

Usage Example

Basic Usage

from stable_datasets.images.cifar100_c import CIFAR100C

# Load the test set (only split available)
ds = CIFAR100C(split="test")

sample = ds[0]
print(sample.keys())  # {"image", "label", "corruption_name", "corruption_level"}
print(f"Corruption: {sample['corruption_name']} at level {sample['corruption_level']}")

# Optional: make it PyTorch-friendly
ds_torch = ds.with_format("torch")

Typical Workflow

from stable_datasets.images.cifar100 import CIFAR100
from stable_datasets.images.cifar100_c import CIFAR100C

# Train on clean CIFAR-100
train_data = CIFAR100(split="train")
# ... train your model ...

# Evaluate robustness on CIFAR-100-C
test_data = CIFAR100C(split="test")
# ... evaluate on corrupted images ...

Related Datasets

• CIFAR-100: Original clean CIFAR-100 dataset for training

• CIFAR-10: Simplified version with 10 classes

• CIFAR-10-C: Corrupted version of CIFAR-10 for robustness evaluation

References

• Paper: Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (ICLR 2019)

• Zenodo: https://zenodo.org/records/3555552

• License: CC BY 4.0

Citation

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