deepinsight/insightface — an AI tool — sits at 30.2k GitHub stars in the AI Tools space. State-of-the-art 2D and 3D Face Analysis Project
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InsightFace project is mainly maintained by Jia Guo and Jiankang Deng.
For more information, please visit our website at https://www.insightface.ai
InsightFace 2.0 Update
InsightFace 2.0 adds PrivateFrame
for local video face blur and mosaic, reference-photo selection, and editable
analysis JSON through desktop, CLI, and Python workflows, plus optional
RGB liveness detection for
FaceAnalysis and InsightFace Server. The Python package
also supports raccoon_s / raccoon_l model packages, automatic CoreML/CUDA/CPU
provider selection, and reusable CoreML compilation caches. PrivateFrame defaults
to Fast mode (target 15 analysis FPS).
PersonAnalysis adds person detection,
face recognition, and body ReID for matching people against registered references.
Use its Python API or try local videos, webcams, and RTSP streams in the desktop GUI.
For installation and use, start with the Python SDK and GUI documentation.
InsightFace Server
The new InsightFace Server provides a simple self-hosted Web UI,
snake_case REST API, and Python client for detection, optional liveness checks,
comparison, registration, and Person search. A single Linux x86_64 CPU or NVIDIA GPU
container runs local ONNX Runtime inference and SQLite with operator-supplied,
manifest-verified models. It is a simple AWS Rekognition alternative with
accuracy-preserving INT8 embedding quantization and 50M+ image search on one RTX 5090 GPU.
License
The code of InsightFace is released under the MIT License. There is no limitation for both academic and commercial usage.
The training data containing the annotation (and the models trained with these data) are available for non-commercial research purposes only.
Both manual-downloading models from our github repo and auto-downloading models with our python-library follow the above license policy(which is for non-commercial research purposes only).
2025-11-24 Update:
For inswapper series face swap models (e.g., inswapper_128.onnx/inswapper-512-live), please contact [email protected] for licensing and additional support.
For open-sourced face recognition models (e.g., buffalo_l package), please contact [email protected] for licensing.
For advanced face recognition SDK and models (e.g., InspireFace SDK), please contact [email protected] for licensing and additional support.
ChangeLogs
2026-10-03InsightFace 2.1PersonAnalysis update: Add person detection, face recognition and body ReID with separate get(), match() and optional update() calls. Support cheetah_s / cheetah_l on CPU or CUDA with automatic model downloads, plus a GUI workflow for local videos, webcams and RTSP with analysis-rate controls and advanced settings. See the Python package and full change log.
2026-09-09InsightFace 2.0Liveness update: Add the optional RGB liveness addon, normal/observe modes, per-face scores and input-rejection reasons, plus Server Web model installation and activation. See Python usage and Server usage.
2026-09-09InsightFace 2.0PrivateFrame update: Add local video detection and tracking, Gaussian blur/mosaic, selective redaction from reference photos, reusable JSON with rendering without another inference pass, and integrated desktop, CLI, and Python API workflows. See the feature and usage guide.
2026-07-27 InsightFace Server Added a simple AWS Rekognition alternative with accuracy-preserving INT8 embedding quantization and 50M+ image search on one RTX 5090 GPU.
2026-05-23InsightFace 1.0 Added a cross-platform desktop GUI Demo for face recognition, enterprise evaluation, reports, and face swap trials, with a lighter default Python install that removes C++ build requirements.
2025-11-18[Picsi.ai] Released Live Face Swap macOS & iOS App and updated Picsi.ai services with our latest series of swap models (incl. inswapper-live/Cyn/Dax).
2024-05-04[Picsi.ai] Released InspireFace, a cross-platform C/C++ face recognition SDK.
2024-04-17: Monocular Identity-Conditioned Facial Reflectance Reconstruction accepted by CVPR-2024.
2023-08-08: We released the implementation of Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views at reconstruction/gaze.
2023-05-03: We have launched the ongoing version of wild face anti-spoofing challenge. See details here.
2023-02-13: We launch a large scale in the wild face anti-spoofing challenge on CVPR23 Workshop, see details at challenges/cvpr23-fas-wild.
2022-11-28: Single line code for facial identity swapping in our python packge ver 0.7, please check the example here.
2022-10-28: MFR-Ongoing website is refactored, please create issues if there's any bug.
2022-09-22: Now we have web-demos: face-localization, face-recognition, and face-swapping.
2022-08-12: We achieved Rank-1st of
Perspective Projection Based Monocular 3D Face Reconstruction Challenge
of ECCV-2022 WCPA Workshop, paper and code.
2022-03-30: Partial FC accepted by CVPR-2022.
2022-02-23: SCRFD accepted by ICLR-2022.
2021-11-30: MFR-Ongoing challenge launched(same with IFRT), which is an extended version of iccv21-mfr.
2021-10-29: We achieved 1st place on the VISA track of NIST-FRVT 1:1 by using Partial FC (Xiang An, Jiankang Deng, Jia Guo).
2021-10-11: Leaderboard of ICCV21 - Masked Face Recognition Challenge released. Video: Youtube, Bilibili.
2021-06-05: We launch a Masked Face Recognition Challenge & Workshop on ICCV 2021.
Introduction
InsightFace is an open source 2D&3D deep face analysis toolbox, mainly based on PyTorch and MXNet.
Please check our website for detail.
The master branch works with PyTorch 1.6+ and/or MXNet=1.6-1.8, with Python 3.x.
InsightFace efficiently implements a rich variety of state of the art algorithms of face recognition, face detection and face alignment, which optimized for both training and deployment.
Quick Start
Please start with our python-package, for testing detection, recognition and alignment models on input images.
Please click the image to watch the Youtube video. For Bilibili users, click here.
Projects
The page on InsightFace website also describes all supported projects in InsightFace.
You may also interested in some challenges hold by InsightFace.
Face Recognition
Introduction
In this module, we provide training data, network settings and loss designs for deep face recognition.
The supported methods are as follows:
ArcFace_mxnet (CVPR'2019)
ArcFace_torch (CVPR'2019)
SubCenter ArcFace (ECCV'2020)
PartialFC_mxnet (CVPR'2022)
PartialFC_torch (CVPR'2022)
VPL (CVPR'2021)
Arcface_oneflow
ArcFace_Paddle (CVPR'2019)
Commonly used network backbones are included in most of the methods, such as IResNet, MobilefaceNet, MobileNet, InceptionResNet_v2, DenseNet, etc..
Datasets
The training data includes, but not limited to the cleaned MS1M, VGG2 and CASIA-Webface datasets, which were already packed in MXNet binary format. Please dataset page for detail.
Evaluation
We provide standard IJB and Megaface evaluation pipelines in evaluation
Pretrained Models
Please check Model-Zoo for more pretrained models.
Third-party Re-implementation of ArcFace
TensorFlow: InsightFace_TF
TensorFlow: tf-insightface
TensorFlow:insightface
PyTorch: InsightFace_Pytorch
PyTorch: arcface-pytorch
Caffe: arcface-caffe
Caffe: CombinedMargin-caffe
Tensorflow: InsightFace-tensorflow
TensorRT: wang-xinyu/tensorrtx
TensorRT: InsightFace-REST
ONNXRuntime C++: ArcFace-ONNXRuntime
ONNXRuntime Go: arcface-go
MNN: ArcFace-MNN
TNN: ArcFace-TNN
NCNN: ArcFace-NCNN
Face Detection
Introduction
In this module, we provide training data with annotation, network settings and loss designs for face detection training, evaluation and inference.
The supported methods are as follows:
RetinaFace (CVPR'2020)
SCRFD (Arxiv'2021)
blazeface_paddle
RetinaFace is a practical single-stage face detector which is accepted by CVPR 2020. We provide training code, training dataset, pretrained models and evaluation scripts.
SCRFD is an efficient high accuracy face detection approach which is initialy described in Arxiv. We provide an easy-to-use pipeline to train high efficiency face detectors with NAS supporting.
Face Alignment
Introduction
In this module, we provide datasets and training/inference pipelines for face alignment.
Supported methods:
SDUNets (BMVC'2018)
SimpleRegression
SDUNets is a heatmap based method which accepted on BMVC.
SimpleRegression provides very lightweight facial landmark models with fast coordinate regression. The input of these models is loose cropped face image while the output is the direct landmark coordinates.
Citation
If you find InsightFace useful in your research, please consider to cite the following related papers:
@inproceedings{ren2023pbidr,
title={Facial Geometric Detail Recovery via Implicit Representation},
author={Ren, Xingyu and Lattas, Alexandros and Gecer, Baris and Deng, Jiankang and Ma, Chao and Yang, Xiaokang},
booktitle={2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)},
year={2023}
}
@article{guo2021sample,
title={Sample and Computation Redistribution for Efficient Face Detection},
author={Guo, Jia and Deng, Jiankang and Lattas, Alexandros and Zafeiriou, Stefanos},
journal={arXiv preprint arXiv:2105.04714},
year={2021}
}
@inproceedings{gecer2021ostec,
title={OSTeC: One-Shot Texture Completion},
author={Gecer, Baris and Deng, Jiankang and Zafeiriou, Stefanos},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}
@inproceedings{an_2022_pfc_cvpr,
title={Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
author={An, Xiang and Deng, Jiangkang and Guo, Jia and Feng, Ziyong and Zhu, Xuhan and Jing, Yang and Tongliang, Liu},
booktitle={CVPR},
year={2022}
}
@inproceedings{an_2021_pfc_iccvw,
title={Partial FC: Training 10 Million Identities on a Single Machine},
author={An, Xiang and Zhu, Xuhan and Gao, Yuan and Xiao, Yang and Zhao, Yongle and Feng, Ziyong and Wu, Lan and Qin, Bin and Zhang, Ming and Zhang, Debing and Fu, Ying},
booktitle={ICCVW},
year={2021},
}
@inproceedings{deng2020subcenter,
title={Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces},
author={Deng, Jiankang and Guo, Jia and Liu, Tongliang and Gong, Mingming and Zafeiriou, Stefanos},
booktitle={Proceedings of the IEEE Conference on European Conference on Computer Vision},
year={2020}
}
@inproceedings{Deng2020CVPR,
title = {RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild},
author = {Deng, Jiankang and Guo, Jia and Ververas, Evangelos and Kotsia, Irene and Zafeiriou, Stefanos},
booktitle = {CVPR},
year = {2020}
}
@inproceedings{guo2018stacked,
title={Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment},
author={Guo, Jia and Deng, Jiankang and Xue, Niannan and Zafeiriou, Stefanos},
booktitle={BMVC},
year={2018}
}
@article{deng2018menpo,
title={The Menpo benchmark for multi-pose 2D and 3D facial landmark localisation and tracking},
author={Deng, Jiankang and Roussos, Anastasios and Chrysos, Grigorios and Ververas, Evangelos and Kotsia, Irene and Shen, Jie and Zafeiriou, Stefanos},
journal={IJCV},
year={2018}
}
@inproceedings{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
booktitle={CVPR},
year={2019}
}
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deepinsight/insightface (deepinsight/insightface) is a Python project on GitHub. From the project's own README: State-of-the-art 2D and 3D Face Analysis Project
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The project maintains a homepage at https://insightface.ai. The README tab on this page also usually contains screenshots and a quickstart.
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