Update README.md

README.md

@@ -1,9 +1,58 @@
 ---
 tags:
+- image-to-3d
 - pytorch_model_hub_mixin
 - model_hub_mixin
+library_name: mast3r
+repo_url: https://github.com/naver/mast3r
 ---
 
-This model has been pushed to the Hub using ****:
-- Repo: [More Information Needed]
-- Docs: [More Information Needed]
+
+## Grounding Image Matching in 3D with MASt3R
+
+```bibtex
+@misc{mast3r_arxiv24,
+      title={Grounding Image Matching in 3D with MASt3R}, 
+      author={Vincent Leroy and Yohann Cabon and Jerome Revaud},
+      year={2024},
+      eprint={TODO},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@inproceedings{dust3r_cvpr24,
+      title={DUSt3R: Geometric 3D Vision Made Easy}, 
+      author={Shuzhe Wang and Vincent Leroy and Yohann Cabon and Boris Chidlovskii and Jerome Revaud},
+      booktitle = {CVPR},
+      year = {2024}
+}
+```
+
+# License
+The code is distributed under the CC BY-NC-SA 4.0 License. See [LICENSE](https://github.com/naver/mast3r/blob/main/LICENSE) for more information.
+For the model, make sure to agree to the license of all the training datasets in addition to CC-BY-NC-SA 4.0, in particular, the [mapfree dataset license](https://research.nianticlabs.com/mapfree-reloc-benchmark/dataset) is very restrictive.
+For the checkpoints, make sure to agree to the license of all the public training datasets and base checkpoints we used, in addition to CC-BY-NC-SA 4.0.
+The mapfree dataset license in particular is very restrictive. For more information, check [CHECKPOINTS_NOTICE](https://github.com/naver/mast3r/blob/main/CHECKPOINTS_NOTICE).
+
+# Model info
+
+Gihub page: https://github.com/naver/mast3r/
+
+| Modelname   | Training resolutions | Head | Encoder | Decoder |
+|-------------|----------------------|------|---------|---------|
+| MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric | 512x384, 512x336, 512x288, 512x256, 512x160 | CatMLP+DPT | ViT-L | ViT-B |
+
+# How to use
+
+First, [install mast3r](https://github.com/naver/mast3r?tab=readme-ov-file#installation).
+To load the model:
+
+```python
+from mast3r.model import AsymmetricMASt3R
+import torch
+
+model = AsymmetricMASt3R.from_pretrained("naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_nonmetric")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+```