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insightface

deepghs / insightface

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Model's Last Updated: February 26 2025
feature-extraction

Introduction of insightface

Model Details of insightface

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.

Runs of deepghs insightface on huggingface.co

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More Information About insightface huggingface.co Model

More insightface license Visit here:

https://choosealicense.com/licenses/model-distribution-disclaimer-license

insightface huggingface.co

insightface huggingface.co is an AI model on huggingface.co that provides insightface's model effect (), which can be used instantly with this deepghs insightface model. huggingface.co supports a free trial of the insightface model, and also provides paid use of the insightface. Support call insightface model through api, including Node.js, Python, http.

insightface huggingface.co Url

https://huggingface.co/deepghs/insightface

deepghs insightface online free

insightface huggingface.co is an online trial and call api platform, which integrates insightface's modeling effects, including api services, and provides a free online trial of insightface, you can try insightface online for free by clicking the link below.

deepghs insightface online free url in huggingface.co:

https://huggingface.co/deepghs/insightface

insightface install

insightface is an open source model from GitHub that offers a free installation service, and any user can find insightface on GitHub to install. At the same time, huggingface.co provides the effect of insightface install, users can directly use insightface installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

insightface install url in huggingface.co:

https://huggingface.co/deepghs/insightface

Url of insightface

insightface huggingface.co Url

Provider of insightface huggingface.co

deepghs
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