Update README.md

README.md

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 license: mit
 language:
 - en
-tags:
-- code
----
+---
+
+# PETAL<i>face</i> Model Card
+
+<div align="center">
+
+[**Project Page**](https://kartik-3004.github.io/PETALface/) **|** [**Paper (ArXiv)**](https://kartik-3004.github.io/PETALface/) **|** [**Code**](https://github.com/Kartik-3004/PETALface)
+
+
+</div>
+
+## Introduction
+
+<div  align="center">
+<img src='assets/visual_abstract.png' height="50%" width="50%">
+</div>
+
+PETALface, is the first work which uses image-quality adaptive LoRA layers for low-resolution face recgonition. The main contributions of our work are:
+1. We introduce the use of the LoRA-based PETL technique to adapt large pre-trained face-recognition models to low-resolution datasets. 
+2. We propose an image-quality-based weighting of LoRA modules to create separate proxy encoders for high-resolution and low-resolution data, 
+ensuring effective extraction of embeddings for face recognition. 
+3. We demonstrate the superiority of PETAL\textit{face} in adapting to low-resolution datasets, outperforming other state-of-the-art models on 
+low-resolution benchmarks while maintaining performance on high-resolution and mixed-quality datasets.
+
+
+
+## Training Framework
+<div  align="center">
+<img src='assets/petalface.png'>
+</div>
+
+Overview of the proposed PETALface approach: We include an additional trainable module in linear layers present in attention layers and the 
+final feature projection MLP. The trainable module is highlighted on the right. Specifically, we add two LoRA layers, where the weightage α is 
+decided based on the input-image quality, computed using an off-the-shelf image quality assessment network (IQA).
+
+
+
+## Usage
+
+The pre-trained weights can be downloaded directly from this repository or using python:
+```python
+from huggingface_hub import hf_hub_download
+
+# Finetuned Weights
+
+# The filename "swin_arcface_webface4m_tinyface" indicates that the model has a swin bakcbone and pretraind
+# on webface4m dataset with arcface loss function and finetuned on tinyface.
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_arcface_webface4m_tinyface/model.pt", local_dir="./weights")
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_cosface_webface4m_tinyface/model.pt", local_dir="./weights")
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_cosface_webface4m_briar/model.pt", local_dir="./weights")
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_cosface_webface12m_briar/model.pt", local_dir="./weights")
+
+# Pre-trained Weights
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_arcface_webface4m/model.pt", local_dir="./weights")
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_cosface_webface4m/model.pt", local_dir="./weights")
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_arcface_webface12m/model.pt", local_dir="./weights")
+hf_hub_download(repo_id="kartiknarayan/petalface", filename="swin_cosface_webface12m/model.pt", local_dir="./weights")
+```
+
+## Citation
+```bibtex
+Coming Soon !
+```
+
+Please check our [GitHub repository](https://kartik-3004.github.io/PETALface/) for complete instructions.