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 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.
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.
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/