---
license: mit
---

The dataset used to train and evaluate [ReT](https://www.arxiv.org/abs/2503.01980) for multimodal information retrieval. The dataset is almost the same as the original M2KR, with a few modifications:
- we exlude any data from MSMARCO, as it does not contain query images;
- we add passage images to OVEN, InfoSeek, E-VQA, and OKVQA. Refer to the paper for more details.


## Sources
- **Repository:** https://github.com/aimagelab/ReT
- **Paper:** [Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval](https://www.arxiv.org/abs/2503.01980) (CVPR 2025)


## Download images (coming soon)
1. Initialize git LFS
```
git lfs install
```

2. Clone the repository (it will take a lot)
```
git clone https://huggingface.co/datasets/aimagelab/ReT-M2KR
```

3. Decompress images (it will take a lot, again)
```
cat ret-img-{000..129}.tar.gz | tar xzf -
```

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@inproceedings{caffagni2025recurrence,
  title={{Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval}},
  author={Caffagni, Davide and Sarto, Sara and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2025}
}
```