# LibrePhotos — Self-Hosted AI-Powered Photo Management

> A self-hosted open-source photo management service with automatic face recognition, object detection, and geolocation tagging powered by machine learning.

## Install

Save in your project root:

# LibrePhotos — Self-Hosted AI-Powered Photo Management

## Quick Use
```bash
git clone https://github.com/LibrePhotos/librephotos-docker.git
cd librephotos-docker
cp .env.example .env
docker compose up -d
# Open http://localhost:3000
```

## Introduction
LibrePhotos is a self-hosted photo management platform that uses machine learning for automatic face recognition, scene classification, and object detection. It provides a Google Photos-like experience while keeping all your images and metadata on your own server, giving you full ownership of your photo library.

## What LibrePhotos Does
- Automatically detects and clusters faces across your photo library for people albums
- Classifies scenes and objects using pre-trained models for smart search
- Extracts and displays EXIF metadata including GPS coordinates on an interactive map
- Generates timeline views with automatic grouping by date and location
- Supports multi-user setups with individual libraries and sharing capabilities

## Architecture Overview
LibrePhotos uses a Django backend with Celery for asynchronous task processing. Machine learning inference runs on-device using PyTorch models for face detection, recognition, and image classification. PostgreSQL stores metadata and user data. The frontend is a React single-page application. Photos are stored on the filesystem and indexed by a background scanner that extracts metadata, generates thumbnails, and runs ML classification pipelines.

## Self-Hosting & Configuration
- Deploy using the official `librephotos-docker` repository with Docker Compose
- Mount your existing photo directories as volumes in the container configuration
- Set `ADMIN_EMAIL` and `ADMIN_PASSWORD` in `.env` for the initial admin account
- Configure the scan directory path where LibrePhotos will discover new photos
- Optionally enable GPU passthrough in Docker for faster ML inference on NVIDIA hardware

## Key Features
- Face detection and recognition with automatic clustering into people albums
- Scene and object classification for intelligent photo search
- Interactive map view using GPS data from photo EXIF metadata
- Background photo scanning with automatic thumbnail generation
- Multi-user support with per-user libraries and sharing

## Comparison with Similar Tools
- **PhotoPrism** — Similar AI features but uses TensorFlow; LibrePhotos uses PyTorch and offers face clustering
- **Immich** — Mobile-first with companion app; LibrePhotos focuses on server-side ML processing
- **Photoview** — Lightweight gallery without ML features; LibrePhotos adds face and object recognition
- **Nextcloud Photos** — Part of a larger ecosystem; LibrePhotos is purpose-built for photo management
- **Piwigo** — Traditional gallery manager; LibrePhotos provides modern AI-driven organization

## FAQ
**Q: How much storage overhead does LibrePhotos add?**
A: Thumbnails and ML embeddings typically add 5-10% on top of your original photo storage.

**Q: Can I use LibrePhotos without a GPU?**
A: Yes, ML inference works on CPU. A GPU speeds up the initial scan but is not required for daily use.

**Q: Does it support RAW photo formats?**
A: LibrePhotos supports common RAW formats through its image processing pipeline and generates viewable thumbnails.

**Q: How does face recognition training work?**
A: You manually confirm or correct a few face clusters, and LibrePhotos refines its recognition model for your library.

## Sources
- https://github.com/LibrePhotos/librephotos
- https://docs.librephotos.com

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Source: https://tokrepo.com/en/workflows/e738ac7c-3cd3-11f1-9bc6-00163e2b0d79
Author: AI Open Source