retinaface-py
RetinaFace: Single-stage Dense Face Localisation in the Wild
Decision gist · record as of 2026-08-14
No, not recommended for new projects. While the package has low install friction and permissive licensing, it is abandoned (last commit February 2023) and will not receive maintenance or updates. For production face detection, use actively maintained alternatives. Consider this package only for research, reference, or legacy system maintenance where the specific RetinaFace architecture is required and you can manage dependency conflicts independently.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch 1.1.0+ and torchvision 0.3.0+; CUDA-capable GPU strongly recommended for inference speed; trained model weights must be obtained separately.
- Low install friction with a pure Python wheel and four common deep-learning dependencies (numpy, torch, torchvision, opencv-python).
- However, the package is abandoned—last commit was 2023-02-14 and no updates since release.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, making it legally safe to adopt. No licensing barriers to integration.
last release 2023-02-14 (1277 days) · last repo commit 2023-02-14
0 known vulnerabilities (OSV.dev, 2026-08-14) · 153,897 downloads/mo, #10,867 on PyPI
Alternatives
Verify before relying
pip install retinaface-py torch torchvision opencv-python
import retinaface_py
# Requires trained model weights and image input; exact API not detailed in fact sheet- Exact public API and function signatures for face detection inference
- Whether pre-trained weights are bundled or must be downloaded separately
- Performance characteristics on modern PyTorch/torchvision versions post-2023
- Compatibility with current CUDA and cuDNN versions
What it is and what it does
RetinaFace-py is a PyTorch port of the RetinaFace face detection algorithm, a single-stage detector designed to localize faces and facial landmarks in unconstrained images. It offers two backbone architectures: MobileNet0.25 for lightweight deployment (model size 1.7M) and ResNet50 for higher accuracy. The package wraps the core detection logic and is intended for integration into face-detection pipelines, particularly on edge devices or resource-constrained environments.
The package depends on numpy, torch, torchvision, and opencv-python for tensor operations, model inference, and image I/O. It is abandoned as of February 2023 with no maintenance since its single release, meaning it will not receive updates for new PyTorch versions, security patches, or API changes in its dependencies. Users should expect potential compatibility issues with modern PyTorch releases and should treat it as a reference implementation rather than a production-ready library.
Use it for
- Detect and extract face regions from images for downstream face recognition or verification tasks.
- Localize facial landmarks (e.g., eyes, nose, mouth) for face alignment preprocessing.
- Deploy lightweight face detection on mobile or edge devices using the MobileNet0.25 backbone.
- Benchmark face detection performance on standard datasets like WIDERFACE or FDDB.
- Integrate into batch image processing pipelines to filter or annotate images containing faces.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, not recommended for new projects.
While the package has low install friction and permissive licensing, it is abandoned (last commit February 2023) and will not receive maintenance or updates. For production face detection, use actively maintained alternatives. Consider this package only for research, reference, or legacy system maintenance where the specific RetinaFace architecture is required and you can manage dependency conflicts independently.
Install
retinaface-py on PyPI
Before you install
Low install friction with a pure Python wheel and four common deep-learning dependencies (numpy, torch, torchvision, opencv-python). However, the package is abandoned—last commit was 2023-02-14 and no updates since release. Maintenance risk is significant for a computer-vision library in a rapidly evolving ecosystem.
Requires PyTorch 1.1.0+ and torchvision 0.3.0+; CUDA-capable GPU strongly recommended for inference speed; trained model weights must be obtained separately.
License in practice
MIT License permits commercial and private use with minimal restrictions, making it legally safe to adopt. No licensing barriers to integration.
Quickstart
pip install retinaface-py torch torchvision opencv-python
import retinaface_py
# Requires trained model weights and image input; exact API not detailed in fact sheetVerify before relying
- Exact public API and function signatures for face detection inference
- Whether pre-trained weights are bundled or must be downloaded separately
- Performance characteristics on modern PyTorch/torchvision versions post-2023
- Compatibility with current CUDA and cuDNN versions
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpytorchtorchvisionopencv-python |
| Maintenance | Abandoned 1,277 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 153,897 / month, #10,867 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: retinaface_py-0.0.2-py3-none-any.whl
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See also retina-face · mtcnn · face-alignment · insightface · facenet-pytorch · face-recognition · deepface · gfpgan · pretrainedmodels · effdet