---
id: dlib
version: "20.0.1"
license: Boost Software License
license_treatment: unclear
maintenance: active
---

# dlib — A toolkit for making real world machine learning and data analysis applications

License: unclear · Maintenance: active · Downloads: 198.0K/mo

## What it is and what it does

dlib is a production-grade C++ toolkit for machine learning and computer vision that exposes its core algorithms through Python bindings. It provides building blocks for classification, regression, and image processing tasks. The package is designed for real-world applications and has been actively maintained since its early releases.

The main trade-off is installation friction: dlib compiles from source on most systems, requiring a C++ compiler and build tools. Once installed, it offers no runtime dependencies, making it lightweight for deployment. It's widely used in production systems and has strong community adoption.

Use it for:

- Build machine learning classifiers on image or tabular data using dlib's algorithms.
- Perform image processing tasks like resizing, filtering, and feature extraction for computer vision workflows.
- Create real-time video analysis applications that require efficient C++ performance with Python ease-of-use.
- Develop data analysis applications combining machine learning with computer vision capabilities.
- Integrate machine learning models into C++ applications through the toolkit's native interface.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

dlib is a C++ machine learning and computer vision toolkit with Python bindings, providing algorithms for image recognition, classification, and data analysis.

Yes, with conditions. dlib is worth installing if you need robust machine learning or computer vision capabilities and can tolerate the compilation overhead during setup. The library is actively maintained, has no known security vulnerabilities, and carries a permissive license. However, ensure your development environment has a working C++ compiler and CMake before attempting installation.

## Install

pip install dlib
uv add dlib
poetry add dlib

## Installing dlib

Before you install: Installation requires compilation from source (high friction). The package is actively maintained with a recent release and strong community adoption, but build dependencies and platform-specific compilation steps may complicate setup on some systems.

License in practice: Licensed under Boost Software License, which permits use in both open-source and closed-source commercial software with minimal restrictions. License treatment is marked unclear in the metadata, so review the actual license terms before relying on this classification.

Quickstart:

pip install dlib

import dlib
detector = dlib.get_frontal_face_detector()

Requires a C++ compiler and CMake to build from source; compilation may take several minutes depending on system resources.

Verify before relying:

- Exact Python version support range (requires_python is unspecified in metadata)
- Whether pre-built wheels are available for all major platforms or if compilation is always required
- Availability and performance impact of optional AVX instruction support mentioned in documentation
- Whether face detection and landmark extraction are available in the Python API or C++-only features

## Package facts

- License: Boost Software License (unclear)
- Python support: unspecified
- Install friction: high
- Maintenance: active
- Downloads: 198.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

machine learning toolkit python, computer vision library, image recognition algorithms, c++ machine learning bindings, face detection library, data analysis toolkit, deep learning c++, computer-vision, machine-learning, cpp-bindings

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