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* Run ONNX models on a Mac's Neural Engine through lighter's plugin provider lighter's lighter.sh/ane device places an ONNX Runtime plugin execution provider in the container. When it is present, the ONNX session setup registers it once and opens sessions on its Neural Engine device, the same place CUDA, ROCm and OpenVINO are chosen, so the onnx detector (and any model that is not pinned to the CPU) runs there with no configuration. The hardware probe reports it as an onnx unit. * docs: hardware decode on an Apple Silicon Mac under lighter A community section on the video decoding page: lighter's lighter.sh/video device, hwaccel_args -c:v h264_v4l2m2m, and why the Raspberry Pi presets decode a single-stream camera in software. The detector docs link to it. * docs: set the lighter decoder per camera when codecs are mixed * docs: the ONNX detector on a Mac's Neural Engine under lighter * Format the Neural Engine provider setup * Fall back to the default providers when the Neural Engine cannot load a model * Apple Silicon ffmpeg presets for lighter's media engine, recommended when it is present
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.