Update README.md

README.md

@@ -1,3 +1,43 @@
----
-license: mit
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+---
+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)
+
+
+## Downloads images
+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}
+}
+```