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---
license: mit
task_categories:
- image-classification
language:
- en
tags:
- imagenet
- corruption
- robustness
- computer-vision
- image-classification
size_categories:
- 1M<n<10M
---
# Corruption Dataset: Gaussian_Noise
## Dataset Description
This dataset contains corrupted versions of ImageNet-1K images using **gaussian_noise** 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: Gaussian_Noise
Adds Gaussian noise to images, simulating sensor noise.
## Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("MarMaster/corruption-gaussian_noise")
# 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
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