README.md

---
pretty_name: GenDS
tags:
- diffusion
- image-restoration
- computer-vision
license: mit
language:
- en
task_categories:
- text-to-image
size_categories:
- 100K<n<1M
---

# [CVPR-2025] GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration

# Dataset Card for GenDS dataset

<!-- Provide a quick summary of the dataset. -->
The **GenDS dataset** is a large dataset to boost the generalization of image restoration models. It is a combination of existing image restoration datasets and 
diffusion-generated degraded samples from **GenDeg**. 

---

## Usage

The dataset is fairly large at ~360GB. We recommend having at least 800GB of free space. To download the dataset, **git-lfs** is required.

### Download Instructions

```bash
# Install git lfs
git lfs install

# Clone the dataset repository
git clone https://huggingface.co/datasets/Sudarshan2002/GenDS.git
cd GenDS

# Pull the parts
git lfs pull
```

### Extract the Dataset:

```bash
# Combine and extract
cat GenDS_part_* > GenDS.tar.gz
tar -xzvf GenDS.tar.gz
```

After extraction, rename ```GenDSFull``` to ```GenDS```.

## Dataset Structure

The dataset includes:

- `train_gends.json`: Metadata for the training data
- `val_gends.json`: Metadata for the validation data

Each JSON file contains a list of dictionaries with the following fields:

```json
{
  "image_path": "/relpath/to/image",
  "target_path": "/relpath/to/ground_truth",
  "dataset": "Source dataset name",
  "degradation": "Original degradation type",
  "category": "real | synthetic",
  "degradation_sub_type": "GenDeg-generated degradation type OR 'Original' (if from existing dataset)",
  "split": "train | val",
  "mu": "mu value used in GenDeg",
  "sigma": "sigma value used in GenDeg",
  "random_sampled": true | false,
  "sampled_dataset": "Dataset name if mu/sigma are not random"
}
```

### Example Usage:

```python
import json

# Load train metadata
with open("/path/to/train_gends.json") as f:
    train_data = json.load(f)

print(train_data[0])
```


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use **GenDS** in your work, please cite:

```bibtex
@article{rajagopalan2024gendeg,
  title={GenDeg: Diffusion-Based Degradation Synthesis for Generalizable All-in-One Image Restoration},
  author={Rajagopalan, Sudarshan and Nair, Nithin Gopalakrishnan and Paranjape, Jay N and Patel, Vishal M},
  journal={arXiv preprint arXiv:2411.17687},
  year={2024}
}
```