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The GStreamer-based create_inference_stream pipeline is depth-4 and clocked by input arrival, so a frame's result round-trips in ~4x the input gap (~150 ms at ~35 fps) even though the accelerator computes a frame in ~21 ms back-to-back. Switch to the vendor's direct ModelInstance.run() runtime API (the same integration pattern as hailo8l/memryx): a synchronous run() with no video pipeline, so per-frame latency is the flat model time (~21 ms measured for YOLO-NAS M 704x576 at 1 instance), independent of scene activity. - _AxeleraRuntimeInference: single ModelInstance plus a worker thread that pops frames strictly FIFO, letterboxes + quantizes (int8, BGR->RGB), runs the model, feeds the six int8 heads through the compiled model's own baked postprocess_graph.onnx (dequant, crop per TensorInfo.padding[3], transpose) and emits (class_id, score, box) rows after a conf threshold + NMS. - Keeps the async contract (send_input/receive_output/shutdown); requests are paired to results by construction through the FIFO queue, so no pixel tokens and no keep-alives. - Model resolution unchanged (preset under AXELERA_BUILD_ROOT, local path, or URL); labels mapped by name via the merged labelmap.
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.