update README content

README.md

@@ -11,4 +11,48 @@ license: cc-by-4.0
 short_description: Multi-Crop Attention-based Classifier
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<div align="center">
+<h1>Paying Attention to Other Animal Detections Improves Camera Trap Classification Models</h1>
+
+<a href="https://arxiv.org/abs/TODO"><img src="https://img.shields.io/badge/arXiv-TO.DO-b31b1b" alt="arXiv"></a>
+<a href='https://huggingface.co/spaces/gdussert/MCA_Classifier'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
+
+
+**[LBBE](https://lbbe.univ-lyon1.fr/fr)**; **[CEFE](https://www.cefe.cnrs.fr/fr/)**; **[LECA](https://leca.osug.fr/)**
+
+
+[Gaspard Dussert](https://gdussert.github.io/), [Vincent Miele](https://vmiele.gitlab.io/), [Simon Chamaillé-Jammes](https://simonchamaillejammes.mystrikingly.com/), [Stéphane Dray](https://sdray.github.io/)
+</div>
+
+```bibtex
+@misc{dussert2025,
+      title={TODO}, 
+      author={TODO},
+      year={2025},
+      eprint={TODO},
+      archivePrefix={arXiv},
+}
+```
+
+## Overview
+
+Multi-Crop Attention-based Classifier uses self-attention to improve classification by levaring the context of other detection within the camera trap sequence. 
+
+GitHub repo is accessible here : [https://github.com/gdussert/MCA_Classifier](https://github.com/gdussert/MCA_Classifier)
+
+Note: Due to the 1GB storage constraint on Hugging Face, the crop_classifier has been converted to float16.
+
+![Gradio Web Interface Preview](images/models.png)
+
+![Gradio Web Interface Preview](images/gradio_screenshot.jpg)
+
+
+## Acknowledgements
+
+This work was granted access to the HPC resources of IDRIS under the allocation 2022-AD010113729 made by GENCI.
+
+Thanks to [LILA BC](https://lila.science/) for hosting the two datasets, [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) and [Snapshot Serengeti](https://lila.science/datasets/snapshot-serengeti), and for all those who contributed to it.
+
+Thanks [MegaDetector](https://github.com/agentmorris/MegaDetector) and [PytorchWildlife](https://github.com/microsoft/CameraTraps): MDv5a was used for preprocessing and MDV6-Ultralytics-YoloV10-Extra is used by the Gradio demo.
+
+Thanks to the amazing [timm](https://github.com/huggingface/pytorch-image-models) repository.