language:en license:mit arxiv:2406.05184 region:us
---
license: mit
language:
- en
---

## Data Card

This dataset repo contains the synthetic images used in the experiments for our NeurIPS 2024 paper [The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better](https://arxiv.org/abs/2406.05184).

We release images generated by Stable Diffusion 1.5, 1.3, and 1.1 using text prompts generated by Llama-2-7B. Please see our paper or our codebase for full details on the synthetic image generation process. The data is released in WebDataset format in tar shards of 2048 images each. Each shard contains files of the following form:

`{id}.png, {id}.json`

Where `{id}.json` contains metadata (text caption used for generation, class name, classification target) describing the synthetic image `{id}.png`. 

Please feel free to contact the authors if you have any questions.

## Links
Paper: [The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better](https://arxiv.org/abs/2406.05184)<br>
Code: [https://github.com/scottgeng00/unmet-promise/](https://github.com/scottgeng00/unmet-promise/)

## Citation
If you find this data useful for your research, please cite our work as
```
@article{geng2024unmet,
  title={The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better},
  author={Geng, Scott and Hsieh, Cheng-Yu and Ramanujan, Vivek and Wallingford, Matthew and Li, Chun-Liang and Koh, Pang Wei and Krishna, Ranjay},
  journal={arXiv preprint arXiv:2406.05184},
  year={2024}
}
```