Update README.md

README.md

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 # 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.
+## Model Description
+This is a PyTorch implementation of [RetinaFace: Single-stage Dense Face Localisation in the Wild](RetinaFace: Single-stage Dense Face Localisation in the Wild) based on [biubug6's implementation](https://github.com/biubug6/Pytorch_Retinaface). 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, but with additional computational overhead. 
+This model returns bounding box locations of each detected face, confidence scores in the face detection, as well as 10 facial landmark keystones.
 
 - **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)
+## Model Details:
+- **Model Type**: Convolutional Neural Network (Mobilenet backbone)
+- **Framework**: pytorch
+- 
+## Model Sources
+- **Repository:** [Py-Feat](https://github.com/cosanlab/py-feat/tree/main/feat/face_detectors/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
 
 ## Evaluation Results
-The model was evaluated on the WIDER FACE dataset
-
-## Limitations and Biases
-While the Retinaface model performs well in many conditions, it may have limitations, including:
-
-Reduced accuracy in detecting faces with heavy occlusions
-Potential biases towards the demographic distribution of the training dataset
-Ethical Considerations
-When using face detection technologies, it is essential to consider the ethical implications, such as privacy concerns and potential biases. Ensure that the use of this model complies with relevant regulations and guidelines.
+The model was evaluated on the WIDER FACE dataset see the benchmark results in [biubug6 repository](https://github.com/biubug6/Pytorch_Retinaface)
 
 ## Citation
 If you use the Retinaface model in your research or application, please cite the following paper:
@@ -58,4 +46,4 @@ If you use the Retinaface model in your research or application, please cite the
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 ## Acknowledgements
-We thank the contributors and the open-source community for their valuable support in developing this model. Special thanks to the authors of the original Retinaface paper and the WIDER FACE dataset.
+We thank the contributors and the open-source community for their valuable support in developing this model. Special thanks to the authors of the original Retinaface paper, the WIDER FACE dataset, and biubug6 for sharing weights and code.