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

CVLFace Pretrained Model (ADAFACE VIT BASE KPRPE WEBFACE4M)

🌎 GitHub • 🤗 Hugging Face

----- ## 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 # helpfer function to download huggingface repo and use model def download(repo_id, path, HF_TOKEN=None): 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) model = AutoModel.from_pretrained(path, trust_remote_code=True, token=HF_TOKEN) os.chdir(cwd) 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 = 'path/to/store/model/locally' 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 keypoints = torch.randn(1, 5, 2) out = model(input, keypoints) ```