A photo management suite for Nextcloud that organizes images and videos using date and location metadata.

Memories is an open-source photo management application that integrates directly into Nextcloud. It organizes images and videos based on date and location metadata, providing a structured way to browse and manage large media libraries. The software is deployed as a Nextcloud app and is accessed via a web browser or a dedicated Android client, while automatic uploads are handled through official Nextcloud mobile applications for Android and iOS.
The application focuses on metadata parsing and indexing to handle high volumes of media. Users can organize their files into albums, manage metadata in bulk, and utilize a timeline view to navigate their history. It supports both local storage within the Nextcloud environment and the import of data from external sources, ensuring that users can consolidate their media collections in one place.
The system architecture consists of a PHP backend for database migrations and a Vue-based frontend. For video processing, it utilizes go-vod, an on-demand transcoder written in Go. The software is designed for users who already operate a Nextcloud instance and require a more advanced gallery than the default options. To ensure stability at scale, the developers have tested the application on instances containing over one million photos, focusing on fast indexing and retrieval of metadata.
For developers, the project is organized as a monorepo containing the PHP library, the Vue frontend, and the Go transcoder. This structure allows for coordinated updates across the backend and the native Android implementation. The application is licensed under the AGPLv3, ensuring it remains open and accessible for the self-hosting community.
Memories serves as a self-hosted media gallery and organization tool for the Nextcloud ecosystem.
Back up, organize, and browse personal photo and video libraries on a self-hosted server without cloud access.
Self-hosted photo and video management application using machine learning for automatic tagging and face recognition.
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