# InsightFace - **Website:** - **Primary alias:** `insightface.ai` ## Description InsightFace is a state-of-the-art deep face analysis library that offers both 2D and 3D face analysis capabilities. This open-source library is an integrated Python library designed for face recognition, face detection, and face alignment. With a focus on optimization for training and deployment, InsightFace provides research institutes and industrial organizations with a range of benefits. InsightFace has received numerous awards and recognition for its outstanding performance. It ranked first in the ECCV 2022 WCPA Challenge and NIST-FRVT 1:1 VISA, and fourth in the NIST-FRVT 1:1. Additionally, it achieved second place in the WIDER Face Detection Challenge 2019 and first place in the iQIYI VID Challenge 2019. This library is not only powerful but also user-friendly, making it accessible for both experienced developers and beginners. It is an open-source framework, which means it can be freely accessed and used. InsightFace can be found on GitHub, where you can explore the project and get started with your own face analysis tasks. To install InsightFace, you need Python 3.6 or higher, and the library can be easily installed using pip. The documentation is readily available for reference. InsightFace offers various projects for different face analysis applications. ArcFace is a cutting-edge face recognition approach, while SubCenter-ArcFace focuses on large-scale noisy web faces. VPL is a face recognition approach based on variational prototype learning, and Partial-FC is a large-scale training framework. For face detection, RetinaFace stands out as a state-of-the-art multi-task approach, and SCRFD is an efficient high-accuracy face detection method. To enhance face alignment, InsightFace provides SDUNet, a stacked dense U-Net approach, and CoordinateReg, an experimental method for fast and accurate inference. InsightFace also hosts several challenges, including the ongoing MFR challenge, the 4th Face Anti-spoofing Workshop and Challenge, the Masked Face Recognition Challenge & Workshop ICCV 2021, and the Lightweight Face Recognition Challenge & Workshop ICCV 2019. These challenges provide opportunities for researchers and developers to showcase their skills and compete in the field of face analysis. In conclusion, InsightFace is a comprehensive and powerful deep face analysis library that offers a range of state-of-the-art algorithms for face recognition, face detection, and face alignment. With its user-friendly nature and open-source availability, it is a valuable resource for both academic research and industrial applications. ## Industries - 🖥 Computers Electronics and Technology - 🖥 Artificial Intelligence and Machine Learning _(under Computers Electronics and Technology)_ ## Links - [github](https://github.com/deepinsight) ## Logos & icons - logo _(primary)_ — PNG — [download](https://cdn.brandfetch.io/idHFueYgCM/w/640/h/640/theme/dark/logo.png?c=1bxid64Mup7aczewSAYMX&t=1765812953714) ## Colors | Name | Hex | Theme | | --- | --- | --- | | Mariner | #2674b5 | accent | | Blue Zodiac | #131f59 | dark | | White | #ffffff | light | ## Fonts - Arial — asset _(custom)_ --- _Source: _ _See [/llms.txt](/llms.txt) for a full list of agent-readable pages, and [/auth.md](/auth.md) for how agents authenticate._