diff --git a/README.md b/README.md
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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: Brightness
+
+## Dataset Description
+
+This dataset contains corrupted versions of ImageNet-1K images using **brightness** 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: Brightness
+
+Adjusts image brightness levels, simulating different lighting conditions.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("marcin-osial/corruption-brightness")
+
+# 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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