retina-face
RetinaFace: Deep Face Detection Framework in TensorFlow for Python
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers low install friction. It is well-suited if you need face detection and landmark extraction as part of a Python computer vision workflow. Install it if you are building a face recognition pipeline or need facial landmarks for alignment; skip it if you only need bounding boxes and can tolerate heavier dependencies like tensorflow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires tensorflow and opencv-python, which may take time to install on first setup depending on system configuration.
- Low install friction with a pure-Python wheel.
- Depends on numpy, Pillow, opencv-python, and tensorflow—all stable, widely-used libraries.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT, a permissive license that allows commercial and private use with minimal restrictions.
last release 2026-06-01 (74 days) · last repo commit 2026-06-01 · 2,023 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 219,069 downloads/mo, #9,329 on PyPI
Alternatives
Verify before relying
pip install retina-face
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img.jpg")
faces = RetinaFace.extract_faces(img_path="img.jpg", align=True)- Whether pre-trained model weights are downloaded automatically on first use and where they are cached.
- Performance characteristics (inference time, memory usage) on typical hardware.
- Accuracy metrics on standard face detection benchmarks beyond the description's crowd-detection claim.
What it is and what it does
RetinaFace is a TensorFlow-based face detection library that identifies faces in images and returns their bounding boxes along with five facial landmarks (both eyes, nose, and mouth corners). It is designed as a preprocessing step for face recognition pipelines, where it can also align detected faces to improve downstream recognition accuracy. The package wraps a re-implementation of the original RetinaFace model from the insightface project, simplifying the API for pip installation while preserving the reference architecture and pre-trained weights.
The library exposes two main functions: detect_faces() returns face coordinates and landmarks with confidence scores, while extract_faces() crops and optionally aligns detected faces for use in recognition workflows. It depends on numpy, Pillow, opencv-python, and tensorflow, making it suitable for environments where those libraries are already present or acceptable as dependencies.
Use it for
- Preprocessing images for a face recognition system by detecting and aligning faces before passing them to a recognition model.
- Extracting facial landmarks for face alignment or geometric analysis in computer vision applications.
- Building a face detection API or service that returns bounding boxes and confidence scores for detected faces in bulk image processing.
- Integrating face detection into a larger pipeline (e.g., with deepface) for end-to-end face verification or identification tasks.
- Analyzing crowd images to locate and extract individual faces for further processing or annotation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers low install friction. It is well-suited if you need face detection and landmark extraction as part of a Python computer vision workflow. Install it if you are building a face recognition pipeline or need facial landmarks for alignment; skip it if you only need bounding boxes and can tolerate heavier dependencies like tensorflow.
Install
retina-face on PyPI
Before you install
Low install friction with a pure-Python wheel. Depends on numpy, Pillow, opencv-python, and tensorflow—all stable, widely-used libraries. Repository is active with a recent commit and no archived status.
Requires tensorflow and opencv-python, which may take time to install on first setup depending on system configuration.
License in practice
Licensed under MIT, a permissive license that allows commercial and private use with minimal restrictions.
Quickstart
pip install retina-face
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img.jpg")
faces = RetinaFace.extract_faces(img_path="img.jpg", align=True)Verify before relying
- Whether pre-trained model weights are downloaded automatically on first use and where they are cached.
- Performance characteristics (inference time, memory usage) on typical hardware.
- Accuracy metrics on standard face detection benchmarks beyond the description's crowd-detection claim.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.5.5 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpygdownPillowopencv-pythontensorflow |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 219,069 / month, #9,329 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: retina_face-0.0.18-py3-none-any.whl
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See also deepface · face-alignment · insightface · mtcnn · retinaface-py · face-recognition · facexlib · face_recognition_models · facenet-pytorch · gfpgan