Update README.md

README.md

@@ -2,8 +2,72 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+license: mit
+library_name: pytorch
 ---
 
-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]
+# FaceNet
+
+## Model Description
+facenet uses an Inception Residual Masking Network pretrained on VGGFace2 to classify facial identities. Facenet also exposes a 512 latent facial embedding space. 
+
+## Model Details
+- **Model Type**: Convolutional Neural Network (CNN)
+- **Architecture**: Inception Residual masking network. Output layer classifies facial identities. Also provides a 512 dimensional representation layer
+- **Input Size**: 112 x 112 pixels
+- **Framework**: PyTorch
+
+## Model Sources
+- **Repository**: [GitHub Repository](https://github.com/timesler/facenet-pytorch/tree/master)
+- **Paper**: [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832)
+
+## Citation
+If you use this model in your research or application, please cite the following paper:
+
+F. Schroff, D. Kalenichenko, J. Philbin. FaceNet: A Unified Embedding for Face Recognition and Clustering, arXiv:1503.03832, 2015.
+
+```
+@inproceedings{schroff2015facenet,
+ title={Facenet: A unified embedding for face recognition and clustering},
+ author={Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},
+ booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+ pages={815--823},
+ year={2015}
+}
+```
+
+## Acknowledgements
+We thank Tim Esler and David Sandberg for sharing their code and training weights with a permissive license. 
+
+## Example Useage
+
+```python
+import numpy as np
+import torch
+import torch.nn as nn
+from feat.identity_detectors.facenet.facenet_model import InceptionResnetV1
+from huggingface_hub import hf_hub_download
+
+device = 'cpu'
+identity_detector = InceptionResnetV1(
+ pretrained=None,
+ classify=False,
+ num_classes=None,
+ dropout_prob=0.6,
+ device=device,
+ )
+identity_detector.logits = nn.Linear(512, 8631)
+identity_model_file = hf_hub_download(repo_id='py-feat/facenet', filename="facenet_20180402_114759_vggface2.pth")
+identity_detector.load_state_dict(torch.load(identity_model_file, map_location=device))
+identity_detector.eval()
+identity_detector.to(device)
+
+# Test model
+face_image = "path/to/your/test_image.jpg" # Replace with your extracted face image that is [224, 224]
+
+# 512 dimensional Facial Embeddings
+identity_embeddings = identity_detector.forward(extracted_faces)
+
+```
+
+