metadata

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
Paper: FaceNet: A Unified Embedding for Face Recognition and Clustering

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

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)