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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
1717f21f69
Targeted follow-ups to the embeddings_manager ORT leak fix that affect all GPU-resident embedding models (Jina text+vision, PaddleOCR det+rec, ArcFace, YOLOv9 license plate). Detection-side YOLO runners are unaffected since CudaGraphRunner does not call compute_cuda_mem_limit. - compute_cuda_mem_limit now checks the cudaMemGetInfo return code instead of trusting that a non-throwing call populated the buffers. Previously a non-zero rc left both pointers at 0, producing gpu_mem_limit=0 and immediate session OOM rather than the documented 4 GB fallback. - The limit also factors in currently-free VRAM (free * 0.9), not just total. On a shared GPU where co-resident embedding sessions have already consumed most of the device, capping at 80% of total still over-allocates. - The CUDA graph fallback path now logs the underlying exception text so failures (cudaErrorStreamCaptureUnsupported, missing libnvrtc, etc.) stop being swallowed by the bare except. Tests cover all three regression paths plus updated existing tests that now require cudaMemGetInfo to return cudaSuccess explicitly. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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Frigate NVR™ - Realtime Object Detection for IP Cameras
EnglishA 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.