| --- |
| 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](https://github.com/deepinsight/insightface). |
|
|
| # How To Use |
|
|
| ```shell |
| pip install dghs-realutils>=0.1.0 |
| ``` |
|
|
| ```python |
| 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. |
|
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|
