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Update docs to reflect actual ZMQ detector performance (~50-80ms)

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CoreML runs 468/662 model nodes with the rest on CPU, yielding
~50-80ms inference rather than the initially estimated ~15ms.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Erich Blume 2026-02-17 18:26:16 -08:00
commit 9c2f69adc3
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docs/changelog.d/frigate-zmq-detector.infra.md
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Add Apple Silicon ZMQ detector for Frigate — inference moves from ONNX CPU (~117ms) to CoreML/Neural Engine (~15ms)
Add Apple Silicon ZMQ detector for Frigate — inference moves from ONNX CPU (~117ms) to CoreML/Neural Engine (~50-80ms)

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docs/reference/services/frigate.md
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@ -49,7 +49,7 @@ Camera credentials are stored in 1Password and synced via [[external-secrets]] t
## Detection
Object detection uses the [apple-silicon-detector](https://github.com/frigate-nvr/apple-silicon-detector), which runs natively on [[indri]] as a LaunchAgent (`mcquack.eblume.frigate-detector`). It communicates with Frigate via ZMQ over TCP (`tcp://host.minikube.internal:5555`), leveraging CoreML and the M1 Neural Engine for ~15ms inference (down from ~117ms with ONNX CPU).
Object detection uses the [apple-silicon-detector](https://github.com/frigate-nvr/apple-silicon-detector) with a YOLOv9-t model (`yolo-generic`, 320x320), running natively on [[indri]] as a LaunchAgent (`mcquack.eblume.frigate-detector`). It communicates with Frigate via ZMQ over TCP (`tcp://host.minikube.internal:5555`), using CoreML with partial Neural Engine acceleration (~50-80ms inference, down from ~117ms with in-pod ONNX CPU).
A `driveway_entrance` zone is configured for alert filtering — only detections in this zone trigger review alerts.