license: other
license_name: model-distribution-disclaimer-license
license_link: https://huggingface.co/spaces/deepghs/RDLicence
pipeline_tag: feature-extraction
tags:
- onnx
- face
ONNX models from insightface project.
How To Use
pip install dghs-realutils>=0.1.0
from realutils.face.insightface import isf_face_batch_similarity, isf_analysis_faces, isf_faces_visualize
image_path = "/your/image/file"
# get the analysis all the faces
faces = isf_analysis_faces(image_path)
print(faces)
# compare them
print(isf_face_batch_similarity([face.embedding for face in faces]))
# visualize it
isf_faces_visualize(image_path, faces).show()
Available Models
We evaluated all these models with some evaluation datasets on face recognition.
- CFPW (500 ids/7K images/7K pairs)[1]
- LFW (5749 ids/13233 images/6K pairs)[2]
- CALFW (5749 ids/13233 images/6K pairs)[3]
- CPLFW (5749 ids/13233 images/6K pairs)[4]
Below are the complete results and recommended thresholds.
- Det: Success rate of face detection and landmark localization.
- Rec-F1: Maximum F1 score achieved in face recognition.
- Rec-Thresh: Optimal threshold determined by the maximum F1 score.
| Model | Eval ALL (Det/Rec-F1/Rec-Thresh) | Eval CALFW (Det/Rec-F1/Rec-Thresh) | Eval CFPW (Det/Rec-F1/Rec-Thresh) | Eval CPLFW (Det/Rec-F1/Rec-Thresh) | Eval LFW (Det/Rec-F1/Rec-Thresh) |
|---|---|---|---|---|---|
| buffalo_l | 99.88% / 98.34% / 0.2203 | 100.00% / 95.75% / 0.2273 | 99.99% / 99.66% / 0.1866 | 99.48% / 96.41% / 0.2207 | 100.00% / 99.85% / 0.2469 |
| buffalo_s | 99.49% / 96.87% / 0.1994 | 99.99% / 94.45% / 0.2124 | 99.65% / 98.64% / 0.1845 | 98.04% / 92.61% / 0.2019 | 100.00% / 99.68% / 0.2314 |
[1] Sengupta Soumyadip, Chen Jun-Cheng, Castillo Carlos, Patel Vishal M, Chellappa Rama, Jacobs David W, Frontal to profile face verification in the wild, WACV, 2016.
[2] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, 2007.
[3] Zheng Tianyue, Deng Weihong, Hu Jiani, Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments, arXiv:1708.08197, 2017.
[4] Zheng, Tianyue, and Weihong Deng. Cross-Pose LFW: A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments, 2018.