README.md

---
language: en
license: mit
arxiv: 2403.14852
---

<div align="center">
<h1>
 CVLFace Pretrained Model (ADAFACE VIT BASE KPRPE WEBFACE4M)
</h1>
</div>


<p align="center">
 🌎 <a href="https://github.com/mk-minchul/CVLface" target="_blank">GitHub</a> • 🤗 <a href="https://huggingface.co/minchul" target="_blank">Hugging Face</a> 
</p>


-----


## 1. Introduction

Model Name: ADAFACE VIT BASE KPRPE WEBFACE4M

Related Paper: KeyPoint Relative Position Encoding for Face Recognition (https://arxiv.org/abs/2403.14852)

Please cite the orignal paper and follow the license of the training dataset.

## 2. Quick Start

```python
from transformers import AutoModel
from huggingface_hub import hf_hub_download
import shutil
import os
import torch
import sys


# helpfer function to download huggingface repo and use model
def download(repo_id, path, HF_TOKEN=None):
 os.makedirs(path, exist_ok=True)
 files_path = os.path.join(path, 'files.txt')
 if not os.path.exists(files_path):
 hf_hub_download(repo_id, 'files.txt', token=HF_TOKEN, local_dir=path, local_dir_use_symlinks=False)
 with open(os.path.join(path, 'files.txt'), 'r') as f:
 files = f.read().split('\n')
 for file in [f for f in files if f] + ['config.json', 'wrapper.py', 'model.safetensors']:
 full_path = os.path.join(path, file)
 if not os.path.exists(full_path):
 hf_hub_download(repo_id, file, token=HF_TOKEN, local_dir=path, local_dir_use_symlinks=False)

 
# helpfer function to download huggingface repo and use model
def load_model_from_local_path(path, HF_TOKEN=None):
 cwd = os.getcwd()
 os.chdir(path)
 sys.path.insert(0, path)
 model = AutoModel.from_pretrained(path, trust_remote_code=True, token=HF_TOKEN)
 os.chdir(cwd)
 sys.path.pop(0)
 return model


# helpfer function to download huggingface repo and use model
def load_model_by_repo_id(repo_id, save_path, HF_TOKEN=None, force_download=False):
 if force_download:
 if os.path.exists(save_path):
 shutil.rmtree(save_path)
 download(repo_id, save_path, HF_TOKEN)
 return load_model_from_local_path(save_path, HF_TOKEN)


if __name__ == '__main__':
 
 HF_TOKEN = 'YOUR_HUGGINGFACE_TOKEN'
 path = os.path.expanduser('~/.cvlface_cache/minchul/cvlface_adaface_vit_base_kprpe_webface4m')
 repo_id = 'minchul/cvlface_adaface_vit_base_kprpe_webface4m'
 model = load_model_by_repo_id(repo_id, path, HF_TOKEN)

 # input is a rgb image normalized.
 from torchvision.transforms import Compose, ToTensor, Normalize
 from PIL import Image
 img = Image.open('path/to/image.jpg')
 trans = Compose([ToTensor(), Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
 input = trans(img).unsqueeze(0) # torch.randn(1, 3, 112, 112)
 
 # KPRPE also takes keypoints locations as input
 aligner = load_model_by_repo_id('minchul/cvlface_DFA_mobilenet', path, HF_TOKEN)
 aligned_x, orig_ldmks, aligned_ldmks, score, thetas, bbox = aligner(input)
 keypoints = orig_ldmks # torch.randn(1, 5, 2)
 out = model(input, keypoints)
```