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  1. Frigate NVR

    Frigate is an open source NVR built around real-time AI object detection. All processing is performed locally on your own hardware, and your camera feeds never leave your home.

  2. Installation | Frigate

    You need to map the default frigate container ports to your local Synology NAS ports that you want to use to access Frigate. There may be other services running on your NAS that are …

  3. Introduction | Frigate

    Frigate Introduction Recommended hardware Planning a New Installation Installation Updating Camera setup Video pipeline Glossary Guides Getting started Configuring go2rtc Home …

  4. Home Assistant Integration | Frigate

    This is potentially useful when Frigate is behind a reverse proxy, and/or when the default stream port is otherwise not accessible to Home Assistant (e.g. firewall rules).

  5. Getting started | Frigate

    In order to review activity in the Frigate UI, recordings need to be enabled. To enable recording video, add the record role to a stream and enable it in the config.

  6. Frigate Configuration

    It is recommended to start with a minimal configuration and add to it as described in this guide and use the built in configuration editor in Frigate's UI which supports validation.

  7. Frigate+

    Frigate now supports facial and license plate recognition in version 0.16 (currently in beta). Frigate+ models make facial and license plate recognition more efficient by detecting the …

  8. Planning a New Installation | Frigate

    Choosing the right hardware for your Frigate NVR setup is important for optimal performance and a smooth experience. This guide will walk you through the key considerations, focusing on the …

  9. Face Recognition | Frigate

    When Frigate detects and attempts to recognize a face, it will appear in the Train tab of the Face Library, along with its associated recognition confidence. From the Train tab, you can assign …

  10. Recommended hardware | Frigate

    Frigate is designed around the expectation that a detector is used to achieve very low inference speeds. Offloading TensorFlow to a detector is an order of magnitude faster and will reduce …