deepghs/insightface
By deepghs · ↓ 0 · ♥ 9 · Updated 2025-02-25
What's in this repo
This page lists the files in deepghs/insightface, including 1k3d68.onnx, 2d106det.onnx, det_10g.onnx and more.
- 10 ONNX models (.onnx files)
- 44 files, including 12 .json, 10 .onnx, 10 .png, 10 .csv, 1 .md
- Last updated 2025-02-25
ONNX files
| File | Size | Download |
|---|---|---|
| buffalo_l/1k3d68.onnx | — | Download |
| buffalo_l/2d106det.onnx | — | Download |
| buffalo_l/det_10g.onnx | — | Download |
| buffalo_l/genderage.onnx | — | Download |
| buffalo_l/w600k_r50.onnx | — | Download |
| buffalo_s/1k3d68.onnx | — | Download |
| buffalo_s/2d106det.onnx | — | Download |
| buffalo_s/det_500m.onnx | — | Download |
| buffalo_s/genderage.onnx | — | Download |
| buffalo_s/w600k_mbf.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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.
Model info
Created: 2025-02-23
Updated: 2025-02-25
View on Hugging Face