---
title: insightface
canonical_url: "https://www.modelscope.cn/models/deepghs/insightface"
md_url: "https://www.modelscope.cn/models/deepghs/insightface.md"
repository: deepghs/insightface
last_updated: 2025-02-26
license: other
pipeline_tag: feature-extraction
tasks:
  - feature-extraction
library_name:
  - onnx
  - pytorch
frameworks:
  - pytorch
downloads: 1398
stars: 5
tags:
  - onnx
  - face
---

# insightface

> insightface - deepghs 在 ModelScope 开源的模型。ONNX models from insightface project.

deepghs/insightface 是 ModelScope 魔搭社区上的feature-extraction模型，采用 other 许可。

- **Repository**: deepghs/insightface
- **License**: other
- **Tasks**: feature-extraction
- **Tags**: onnx, face
- **Downloads**: 1398
- **Stars**: 5
- **Last updated**: 2025-02-26

Source: https://www.modelscope.cn/models/deepghs/insightface

---

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.