InsightFace is a comprehensive open-source toolbox for 2D and 3D face analysis, offering state-of-the-art algorithms for face recognition, detection, and alignment. It targets researchers and developers in computer vision and AI, providing optimized implementations for both training and deployment.

How It Works

The project leverages deep learning models, primarily built with PyTorch and MXNet, to achieve high performance in face analysis tasks. It implements various advanced techniques like ArcFace, PartialFC, and RetinaFace, focusing on efficient training and robust inference. The architecture is designed for modularity, allowing easy integration of different network backbones and loss functions.

Quick Start & Requirements

• Install: pip install insightface
• Prerequisites: Python 3.x, PyTorch 1.6+ and/or MXNet 1.6-1.8. GPU acceleration is recommended for optimal performance.
• Resources: Pre-trained models are available for download.
• Docs: InsightFace Website

Highlighted Details

• Achieved 1st place in NIST-FRVT 1:1 (VISA track) and Rank-1st in ECCV-2022 WCPA Workshop for 3D face reconstruction.
• Offers advanced face-swapping models (e.g., inswapper_cyn, inswapper_dax) that outperform many commercial alternatives.
• Includes InspireFace, a cross-platform C/C++ face recognition SDK.
• Supports a wide range of face recognition methods (ArcFace, SubCenter ArcFace, PartialFC) and detection methods (RetinaFace, SCRFD).

Maintenance & Community

Maintained by Jia Guo and Jiankang Deng, with significant contributions from Xiang An, Jack Yu, and Baris Gecer. The project has hosted several challenges and workshops, indicating active community engagement.

Licensing & Compatibility

The code is released under the MIT License, permitting both academic and commercial usage. However, training data and models are restricted to non-commercial research purposes only.

Limitations & Caveats

While the code is MIT licensed, the use of provided pre-trained models and datasets is restricted to non-commercial research. This dual licensing could pose challenges for commercial applications that rely on the provided models.