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README.md

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-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
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-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# 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