# Retinaface

## Model Details

### Model Description
Retinaface is a state-of-the-art face detection model built using PyTorch. It accurately detects faces in images and returns bounding boxes around detected faces. The model is designed to work efficiently on a wide range of images, including those with varying lighting conditions, occlusions, and face orientations.

- **License:** MIT
- **License Link:** [MIT License](https://github.com/biubug6/Pytorch_Retinaface/blob/master/LICENSE.MIT)

### Model Sources
- **Repository:** [Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)
- **Paper:** [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641)

## Model Architecture
The Retinaface model utilizes a deep convolutional neural network architecture with multiple layers. It uses `mobilenet0.25` as the backbone network (only 1.7M parameters) but can also use `resnet50` as the backbone to achieve better results. It includes additional layers for feature extraction and bounding box prediction.

## Intended Use
This model is intended for use in applications requiring face detection, such as:
- Security systems
- Augmented reality
- Image processing pipelines
- Photo management applications

## Citation
**BibTeX:** @misc{deng2019retinafacesinglestagedenseface,
      title={RetinaFace: Single-stage Dense Face Localisation in the Wild}, 
      author={Jiankang Deng and Jia Guo and Yuxiang Zhou and Jinke Yu and Irene Kotsia and Stefanos Zafeiriou},
      year={2019},
      eprint={1905.00641},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1905.00641