face_recognition_models
Models used by the face_recognition package.
Decision gist · record as of 2026-08-14
No, unless you are explicitly required by face_recognition as a dependency. The package is abandoned, its Python version claims are obsolete, and installation is friction-heavy due to large model files. If you need facial recognition, evaluate whether face_recognition itself is still maintained and whether its current dependencies have been updated; this package will likely be pulled in automatically if needed, but do not install it directly.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Large model file downloads create significant installation time and disk space requirements.
- High install friction due to large model files.
- Package is abandoned—last release was 2017-09-28 and last commit 2022-12-26.
License · maintenance · safety
MIT license (permissive) — Licensed under MIT (permissive), allowing commercial and private use with attribution. Models themselves are in the public domain or CC0 1.0 Universal per the description, so licensing constraints are minimal.
last release 2017-09-28 (3242 days) · last repo commit 2022-12-26 · 446 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 132,298 downloads/mo, #11,557 on PyPI
Alternatives
Verify before relying
pip install face-recognition-models==0.3.0
# Models are data files only; typically imported indirectly via face_recognition- Whether the package actually works with Python versions beyond 3.6 despite classifier claims
- Current size and download time for the bundled model files
- Whether models are compatible with current versions of dependencies
What it is and what it does
This package is a model repository—it bundles pre-trained neural network weights for face detection, recognition, and encoding tasks. It exists as a dependency for the face_recognition library and is not meant to be used standalone; you install it to get the trained models that face_recognition needs to operate. The models were created by Davis King and are distributed in the public domain or under CC0 1.0 Universal.
The package itself contains no code logic, only data files. Installation requires downloading large model artifacts, which creates significant friction. The project is abandoned and has not been updated since 2017, with the last commit in late 2022 likely being a minor maintenance action. The declared Python support (2.6 through 3.6) is obsolete, and whether it works with modern Python versions is unclear.
Use it for
- Satisfy the model dependency when installing face_recognition for facial detection and recognition tasks
- Provide pre-trained weights for face encoding in computer vision pipelines
- Bundle models for deployment of face recognition applications without requiring separate model downloads
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless you are explicitly required by face_recognition as a dependency.
The package is abandoned, its Python version claims are obsolete, and installation is friction-heavy due to large model files. If you need facial recognition, evaluate whether face_recognition itself is still maintained and whether its current dependencies have been updated; this package will likely be pulled in automatically if needed, but do not install it directly.
Install
face-recognition-models on PyPI
Before you install
High install friction due to large model files. Package is abandoned—last release was 2017-09-28 and last commit 2022-12-26. Classifiers declare support for Python 2.6 through 3.6, which are all end-of-life; actual compatibility with modern Python versions is unverified.
Large model file downloads create significant installation time and disk space requirements.
License in practice
Licensed under MIT (permissive), allowing commercial and private use with attribution. Models themselves are in the public domain or CC0 1.0 Universal per the description, so licensing constraints are minimal.
Quickstart
pip install face-recognition-models==0.3.0
# Models are data files only; typically imported indirectly via face_recognition
Verify before relying
- Whether the package actually works with Python versions beyond 3.6 despite classifier claims
- Current size and download time for the bundled model files
- Whether models are compatible with current versions of dependencies
Package facts
| License | MIT license permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Abandoned 3,242 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 132,298 / month, #11,557 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 2 - Pre-AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 2Programming Language :: Python :: 2.6Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6 |
Evidence: face_recognition_models-0.3.0.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “face recognition models”
- face_recognition_modelsProvides pre-trained deep learning models for face detection,…
- facenet-pytorchProvides pretrained PyTorch models for face detection using MTCNN and…
- insightfaceInsightFace is a Python library for face detection, recognition,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also face-recognition · deepface · retina-face · mtcnn · facenet-pytorch · facexlib · face-alignment · pytorchcv · dlib · smplx