- Add driveway zone from UI config with review detection filtering
- Upgrade model from YOLOv9-t (7.7M params) to YOLOv9-m (51M params)
for improved nighttime/low-light detection capability
- YOLOv9-t produced max 5.7% confidence on nighttime IR footage
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
YOLO-NAS has dynamic output shapes incompatible with CoreML EP, and the
apple-silicon-detector has no yolonas post-processor. YOLOv9-t works with
CoreML and uses the supported yolo-generic model type.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Runs the detector script directly via `uv run --with` with inline
dependencies, eliminating the need for `make install` and a managed venv.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Moves object detection from ONNX CPU (~117ms/frame) to the
apple-silicon-detector running natively on indri via CoreML/Neural Engine
(~15ms), communicating with Frigate over ZMQ (tcp://host.minikube.internal:5555).
- New ansible role `frigate_detector` with LaunchAgent
- Switch Frigate configmap from ONNX to ZMQ detector
- Remove detect FPS cap (no longer needed with fast inference)
- Update docs and add changelog fragment
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
