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* add embedded mode to BaseSection so parents can host the save action * add optional action slot to current Frigate+ model summary * add w-full to action slot flex wrapper for explicit width contract * i18n * merged detectors and model settings view * fix document title * Embed detector form in merged settings view * add detection model card with tabs and custom model embed * add Frigate+ model selector with filter popover to merged page * Add mismatch banner and gate save on detector and model compatibility * Wire atomic save, restart toast, and undo on detectors and model page * Clear child pending data on undo * route merged detectors and model view in settings * trim Frigate+ page to account-only and remove old detection model view * basic e2e * Fix unsaved-changes guard, custom path leak, and post-failure cache resync * Rename to Detectors and model, float Modified badge, use ConfigMessageBanner for mismatch * Hide Plus/Custom tabs when Frigate+ is not enabled * Detect active Plus model via model.plus.id instead of path prefix * Sync state back to snapshot when child form un-modifies and remount on undo * Always require restart on save since model changes also need one * Wrap Frigate+ model selector in SplitCardRow with label and description * rename tab * update docs * sync top-level model with default detector's resolved model when the user doesn't define a top-level `model:` block, `FrigateConfig.model` stayed at pydantic field defaults (320×320, /labelmap.txt) while the per-detector model picked up `DEFAULT_MODEL` for openvino on cpu (300×300, coco_91cl_bkgr.txt introduced in #23127), causing `RemoteObjectDetector` to fail with "buffer is too small for requested array" because the SHM was sized from the per-detector model but mapped using the top-level one. After the detector loop, copy the first detector's resolved model up to `self.model` so both sides agree on dimensions and labelmap * revert to cpu detector by default use openvino cpu for new configs only * add defaults
Frigate NVR™ - Realtime Object Detection for IP Cameras
[English] | 简体中文
A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- Tight integration with Home Assistant via a custom component
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
- Object detection with TensorFlow runs in separate processes for maximum FPS
- Communicates over MQTT for easy integration into other systems
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTSP to reduce the number of connections to your camera
- WebRTC & MSE support for low-latency live view
Documentation
View the documentation at https://docs.frigate.video
Donations
If you would like to make a donation to support development, please use Github Sponsors.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
Screenshots
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
Copyright © 2026 Frigate, Inc.