Update README.md

README.md

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----
-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# FAINT
+
+Fast, Appearance-Invariant Navigation Transformer (FAINT) is a learned policy for vision-based topological navigation.
+
+Please see the [Project Page]() for more information.
+
+## Model Details
+
+The `FAINT-Sim` model uses [`Theia-Tiny-CDDSV`](https://theia.theaiinstitute.com/) as backbone, and was trained for 10 rounds of DAgger with ~12M samples from the Habitat simulator.
+It is capable of zero-shot transfer for navigation with real robots.
+
+This repo contains two versions of the trained model weights.
+- `model_pytorch.pt`: Weights-only state dict of the Pytorch model.
+- `model_torchscript.pt`: A 'standalone' Torchscript model for deployment.
+
+## Usage
+
+See the main Github [repo]() for details, input preprocessing etc.
+
+### Torchscript
+
+Only dependency is Pytorch.
+
+```bash
+import torch
+ckpt_path = 'FAINT-Sim/model_torchscript.pt'
+model = torch.jit.load(ckpt_path)
+```
+
+### Pytorch
+
+Need to have the Faint library installed.
+
+```bash
+import torch
+from faint.common.models.faint import FAINT
+
+ckpt_path = 'FAINT-Sim/model_pytorch.pt'
+state_dict = torch.load(ckpt_path)
+
+model = FAINT() # The weights in this repo correspond to FAINT initialized with the default arguments
+model.load_state_dict(state_dict)
+```
+
+## Citation
+
+If you use FAINT in your research, please use the following BibTeX entry:
+```bibtex
+@article{Suomela_2025_Outperforming,
+  title={Outperforming Real-World Data for Training Vision-Based Navigation Policies},
+  author={Suomela, Lauri and Kuruppu Arachchige, Sasanka and Torres, German F. and Edelman, Harry and Kämäräinen, Joni-Kristian}
+  journal={arXiv:},
+  year={2025}
+}
+```