---
license: mit
task_categories:
- image-classification
language:
- en
tags:
- imagenet
- corruption
- robustness
- computer-vision
- image-classification
size_categories:
- 1M<n<10M
---

# Corruption Dataset: Pixelate

## Dataset Description

This dataset contains corrupted versions of ImageNet-1K images using **pixelate** corruption. It is part of the ImageNet-C benchmark for evaluating model robustness to common image corruptions.

### Dataset Structure

- **Train**: 1,281,167 corrupted images
- **Validation**: 50,000 corrupted images  
- **Classes**: 1000 ImageNet-1K classes
- **Format**: Arrow (Hugging Face Datasets)

### Corruption Type: Pixelate

Reduces image resolution by pixelation.

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("MarMaster/corruption-pixelate")

# Access train and validation splits
train_dataset = dataset["train"]
val_dataset = dataset["validation"]

# Example usage
for example in train_dataset:
    image = example["image"]
    class_id = example["class_id"]
    filename = example["filename"]
```

## Dataset Statistics

- **Total Images**: 1,331,167
- **Train Images**: 1,281,167
- **Validation Images**: 50,000
- **Classes**: 1000
- **Image Format**: RGB
- **Average Image Size**: Variable (ImageNet-1K standard)

## Citation

If you use this dataset, please cite the original ImageNet-C paper:

```bibtex
@article{hendrycks2019benchmarking,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Hendrycks, Dan and Dietterich, Tom},
  journal={Proceedings of the International Conference on Learning Representations},
  year={2019}
}
```

## License

This dataset is released under the MIT License. The original ImageNet dataset follows its own licensing terms.

## Contact

For questions or issues, please contact: marcin.osial@[your-institution].edu