Update README.md

README.md

@@ -1,3 +1,65 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+---
+
+
+
+# MP_FaceMesh_V2
+
+## Model Description
+MP_FaceMesh_V2 is a pytorch port of tensorfolow [FaceMeshV2](https://ai.google.dev/edge/mediapipe/solutions/vision/face_landmarker/index) model from Google's [mediapipe](https://github.com/google-ai-edge/mediapipe) library.
+The model takes a cropped 2D face with 25% margin on each side resized to 256 x 256 pixels and outputs a dense 473 landmark coordinates in a 3D (x,y,z) coordinate space.
+
+The original tensorflow model was ported to ONNX and then to pytorch using [onnx2torch](https://github.com/ENOT-AutoDL/onnx2torch). Currently, we are serializing the converted model, which requires onnx2torch as a dependency. 
+
+See the mediapipe [model card](https://storage.googleapis.com/mediapipe-assets/Model%20Card%20Blendshape%20V2.pdf) for more details.
+
+## Model Details
+- **Model Type**: Convolutional Neural Network (MobileNetV2-like)
+- **Framework**: pytorch
+
+## Model Sources
+- **Repository**: [GitHub Repository](https://github.com/cosanlab/py-feat)
+- **Model Card**: [Attention Mesh: High-fidelity Face Mesh Prediction in Real-time](https://storage.googleapis.com/mediapipe-assets/Model%20Card%20MediaPipe%20Face%20Mesh%20V2.pdf)
+- **Paper**: [Mediapipe FaceMesh model card](https://arxiv.org/abs/2006.10962)
+## Citation
+If you use the mp_facemesh_v2 model in your research or application, please cite the following paper:
+
+Grishchenko, I., Ablavatski, A., Kartynnik, Y., Raveendran, K., & Grundmann, M. (2020). Attention mesh: High-fidelity face mesh prediction in real-time. arXiv preprint arXiv:2006.10962.
+
+```
+@misc{grishchenko2020attentionmeshhighfidelityface,
+      title={Attention Mesh: High-fidelity Face Mesh Prediction in Real-time}, 
+      author={Ivan Grishchenko and Artsiom Ablavatski and Yury Kartynnik and Karthik Raveendran and Matthias Grundmann},
+      year={2020},
+      eprint={2006.10962},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2006.10962}, 
+}
+```
+
+## Example Useage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+device = 'cpu'
+
+# Load model and weights
+landmark_model_file = hf_hub_download(repo_id='py-feat/mp_facemesh_v2', filename="face_landmarks_detector_Nx3x256x256_onnx.pth")
+landmark_detector = torch.load(landmark_model_file, map_location=device, weights_only=False)
+landmark_detector.eval()
+landmark_detector.to(device)
+
+
+# Test model
+face_image = "path/to/your/test_image.jpg"  # Replace with your extracted face image that is [224, 224]
+
+# Extract Landmarks
+landmark_results = landmark_detector(torch.tensor(face_image).to(device))
+        
+```