facexlib
Basic face library
Decision gist · record as of 2026-08-14
Yes, if you need multiple face analysis functions and can accept dormant maintenance. The package offers genuine convenience by aggregating well-known algorithms into one interface with low install friction. However, verify that the original licenses of the specific functions you use align with your project, and be aware that no active development means you may need to fork or patch for compatibility with newer PyTorch or CUDA versions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- PyTorch >= 1.7 required; Python >= 3.7 recommended.
- Pre-trained models download automatically on first inference; stable network connection recommended.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
Apache License 2.0 (permissive) — Released under Apache License 2.0 (permissive). However, the package aggregates algorithms from multiple sources with different licenses (MIT, Apache 2.0, CC 4.0, GPL 3.0); you must review the original licenses of the specific functions you use, as noted in the documentation.
last release 2023-04-15 (1217 days) · last repo commit 2024-02-29 · 976 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,167,161 downloads/mo, #4,276 on PyPI
Alternatives
Verify before relying
pip install facexlib
import facexlib
# Pre-trained models download automatically on first use
# Example: detector = facexlib.detection.init_detection_model(...)- Whether pre-trained model downloads work reliably from current sources or if mirrors/fallbacks are needed.
- Compatibility with PyTorch versions beyond 1.7 and current CUDA releases.
- Performance and accuracy of each function relative to their original implementations.
What it is and what it does
Facexlib is a collection library that wraps state-of-the-art open-source face analysis algorithms into a unified PyTorch interface. It provides nine face-related capabilities—detection, alignment, recognition, parsing, matting, head pose estimation, tracking, quality assessment, and utility helpers—each sourced from a different reference implementation and aggregated under one package.
The library is designed for practitioners who need ready-to-use face functions without implementing each algorithm from scratch. It handles pre-trained model management automatically, downloading weights on first use. Because it aggregates multiple algorithms with different original licenses (MIT, Apache 2.0, CC 4.0, GPL 3.0), users must verify the license terms of each function they use against their intended application.
Use it for
- Build a face detection and alignment pipeline for preprocessing images before feeding to downstream models.
- Perform face recognition or identity verification using the integrated InsightFace algorithm.
- Segment facial regions (parsing) to identify eyes, nose, mouth, and other anatomical parts.
- Estimate head pose angles for gaze direction or 3D face reconstruction tasks.
- Track faces across video frames using the integrated SORT tracker.
- Assess face image quality to filter low-quality samples before processing.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need multiple face analysis functions and can accept dormant maintenance.
The package offers genuine convenience by aggregating well-known algorithms into one interface with low install friction. However, verify that the original licenses of the specific functions you use align with your project, and be aware that no active development means you may need to fork or patch for compatibility with newer PyTorch or CUDA versions.
Install
facexlib on PyPI
Before you install
Low install friction with a pure-Python wheel. Dormant maintenance (last commit 2024-02-29, 1217 days since release) means no recent updates, though the repository remains active with 976 stars. Expect to manage pre-trained model downloads yourself if network connectivity is unreliable.
PyTorch >= 1.7 required; Python >= 3.7 recommended. Pre-trained models download automatically on first inference; stable network connection recommended.
License in practice
Released under Apache License 2.0 (permissive). However, the package aggregates algorithms from multiple sources with different licenses (MIT, Apache 2.0, CC 4.0, GPL 3.0); you must review the original licenses of the specific functions you use, as noted in the documentation.
Quickstart
pip install facexlib
import facexlib
# Pre-trained models download automatically on first use
# Example: detector = facexlib.detection.init_detection_model(...)Verify before relying
- Whether pre-trained model downloads work reliably from current sources or if mirrors/fallbacks are needed.
- Compatibility with PyTorch versions beyond 1.7 and current CUDA releases.
- Performance and accuracy of each function relative to their original implementations.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesfilterpynumbanumpyopencv-pythonPillowscipytorchtorchvisiontqdm |
| Maintenance | Dormant 1,217 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,167,161 / month, #4,276 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8 |
Evidence: facexlib-0.3.0-py3-none-any.whl
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