py-feat/retinaface_r34

RetinaFace ResNet34

Model Description

RetinaFace is a single-shot face detector that jointly predicts bounding boxes, 5-keypoint landmarks (eyes, nose, mouth corners), and a face/no-face score per anchor. This py-feat distribution uses a ResNet34 backbone trained on WIDERFACE, reaching 88.9% AP on WIDERFACE-Hard versus img2pose's 55.5% (per Cheong et al., Affective Science 2023). Postprocessing (priors, decode, NMS) runs fully batched on-device via torchvision.ops.batched_nms. Available in py-feat ≥ 0.7 as Detector(face_model='retinaface_r34') and MPDetector(face_model='retinaface').

Model Details

• Model Type: Convolutional Neural Network (CNN), single-shot face detector
• Architecture: ResNet34 backbone + Feature Pyramid Network + SSH context modules + 3 heads (Class / Bbox / 5-keypoint Landmark)
• Input Size: any spatial resolution (anchors generated per (H, W) and cached)
• Framework: PyTorch
• Training data: WIDERFACE (Yang et al., 2016)

Model Sources

• Repository (port used by py-feat): yakhyo/retinaface-pytorch
• Repository (original PyTorch reference): biubug6/Pytorch_Retinaface
• Paper: RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild

Citation

If you use this model in your research or application, please cite the following paper:

J. Deng, J. Guo, E. Ververas, I. Kotsia, S. Zafeiriou. RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild, CVPR, 2020, arXiv:1905.00641.

@inproceedings{deng2020retinaface,
  title={RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild},
  author={Deng, Jiankang and Guo, Jia and Ververas, Evangelos and Kotsia, Irene and Zafeiriou, Stefanos},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={5203--5212},
  year={2020}
}

Acknowledgements

We thank Yakhyokhuja Valikhujaev for the ResNet34-backbone PyTorch implementation, biubug6 for the original PyTorch reference, and the WIDERFACE authors (Yang, Luo, Loy, Tang) for the training data — all distributed under permissive terms.

Example Useage

import torch
from feat.face_detectors.Retinaface.Retinaface_test import Retinaface
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from feat.utils.io import get_resource_path

device = 'cpu'
detector = Retinaface(device=device)  # py-feat helper that lazy-fetches weights from py-feat/retinaface_r34

# Or, to load weights directly:
weights_file = hf_hub_download(
    repo_id="py-feat/retinaface_r34",
    filename="model.safetensors",
    cache_dir=get_resource_path(),
)
state_dict = load_file(weights_file)
# ... pass to your own RetinaFace module instance.