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---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
license: mit
library_name: py-feat
pipeline_tag: image-feature-extraction
---
# 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)
```
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