metadata
license: mit
tags:
- transformers
language:
- en
FaceXFormer Model Card
Introduction
FaceXFormer is an end-to-end unified model capable of handling a comprehensive range of facial analysis tasks such as face parsing, landmark detection, head pose estimation, attributes recognition, age/gender/race estimation and landmarks visibility prediction.
Model Details
FaceXFormer is a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework.
Usage
The models can be downloaded directly from this repository or using python:
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="kartiknarayan/facexformer", filename="ckpts/model.pt", local_dir="./")
Citation
@misc{narayan2024facexformer,
title={FaceXFormer : A Unified Transformer for Facial Analysis},
author={Kartik Narayan and Vibashan VS and Rama Chellappa and Vishal M. Patel},
year={2024},
eprint={2403.12960},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Please check our GitHub repository for complete inference instructions.