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# Dataset Card for Tiny-ImageNet-C
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
In Tiny ImageNet-C, there are 75,109 corrupted images derived from the original Tiny ImageNet dataset. The images are affected by two different corruption types at five severity levels.
- **License:** CC BY 4.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Homepage:** https://github.com/hendrycks/robustness
- **Paper:** Hendrycks, D., & Dietterich, T. (2019). Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
Total images: 75,109
Classes: 200 categories
Splits:
- **Test:** 75,109 images
Image specs: JPEG format, 64×64 pixels, RGB
## Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
```
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/tiny-imagenet-c", split="test", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]
image.show() # Display the image
print(f"Label: {label}")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
@article{hendrycks2019benchmarking,
title={Benchmarking neural network robustness to common corruptions and perturbations},
author={Hendrycks, Dan and Dietterich, Thomas},
journal={arXiv preprint arXiv:1903.12261},
year={2019}
}
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