Update README.md

README.md

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+# Hyp-OC Model Card
+
+<div align="center">
+
+[**Project Page**]() **|** [**Paper (ArXiv)**]() **|** [**Code**]()
+
+
+</div>
+
+## Introduction
+
+Hyp-OC, is the first work exploring hyperbolic embeddings for one-class face anti-spoofing (OC-FAS). 
+We show that using hyperbolic space helps learn a better decision boundary than the Euclidean counterpart, 
+boosting one-class face anti-spoofing performance.
+
+<div  align="center">
+<img src='assets/intro_viz.png'>
+</div>
+
+## Training Framework
+
+Overview of the proposed pipeline: Hyp-OC. The encoder extracts facial features which are used to estimate the mean of Gaussian 
+distribution utilized to sample pseudo-negative points. The real features and pseudo-negative features are then concatenated 
+and passed to FCNN for dimensionality reduction. The low-dimension features are mapped to Poincaré Ball using *exponential map*. 
+The training objective is to minimize the summation of the proposed loss functions Hyp-PC} and Hyp-CE. The result is a separating 
+*gyroplane* beneficial for one-class face anti-spoofing.
+
+<div  align="center">
+<img src='assets/main_archi.png'>
+</div>
+
+## Usage
+
+The pre-trained weights can be downloaded directly from this repository or using python:
+```python
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="pretrained_weights/vgg_face_dag.pth", local_dir="./")
+```
+
+## Citation
+```bibtex
+Coming soon ...
+```
+
+Please check our [GitHub repository]() for complete instructions.