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- strict per-frame request/result pairing: the feeder registers a queue of pending frame ids, the pump pops one per stream result and stores the outcome under it, and run() waits for its own frame's id. Primer and keep-alive results are dropped instead of being served to callers. - copy frames out of shared memory before queuing them (detect_raw returns before the feeder consumes the frame, unmapping the camera buffer). - raise on inference timeout so the watchdog can restart a stalled stream instead of silently serving zeros. - use ModelDownloader.download_from_url for URL models (.part file, atomic rename, progress events); drop the unused AX_DEFAULT_URL constant. - is_url() requires a scheme; treat www.-style values as paths and fail with a clear FileNotFoundError instead of a deep urlretrieve error, mirroring hailo8l behavior for missing model files. - guard detections against numpy arrays, drop the dead score initialization, floor normalized box coords at 0, move the model-required check before super().__init__(), and document detect_raw/close. - deduplicate the model-compilation walkthrough in docs (keep installation.md as the single source; object_detectors.md links to it).
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.