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Add a notice registry and System Health tab (#24178)

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* add a notice registry and System Health tab

Problems Frigate detects on its own (ffmpeg crash loops, stuck detectors, failed model downloads, recordings deleted before their retention period) only ever existed as log lines. This adds a `NoticeRegistry` in the main process backed by two tables, an `update_notice` IPC topic so producers in other processes can reach it through the dispatcher, an admin-only API and websocket topic, and a Health tab that lists them. Kinds declare their own mode, severity, and category in one place: state notices are resolved by their producer, event notices are dismissed by the user.

* treat a prerelease as behind its final release

* fix notices clearing early

* rename menu items and update docs

* don't resolve the update notice on a failed version lookup
This commit is contained in:
Josh Hawkins
2026-09-12 07:30:04 -06:00
committed by Nicolas Mowen
parent af60d2db48
commit f3a31e2fb4
55 changed files with 2346 additions and 117 deletions
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docs/docs/configuration/object_detectors.md
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@@ -134,7 +134,7 @@ Along with picking a detector for your hardware, you will choose a model's **inp
**Resolution (320x320 vs 640x640):** Frigate is optimized for `320x320` models, and `320x320` is the best choice for the vast majority of setups. Frigate is specifically designed to compensate for the smaller model by cropping a region of motion from the full frame and zooming into it before running detection, so a `320x320` model is actually _better_ at small and distant objects, not worse. A `640x640` model is slower and uses more resources, and its main benefit is fitting more objects into a single inference when many objects are spread across a large area. Recent versions of Frigate have improved support for `640x640` models, but `320x320` remains the recommended starting point for nearly all setups.
**Variant size (tiny/small/medium):** Larger variants are gradually more accurate but slower. Whether the difference is noticeable depends on your specific cameras and scenes. A good rule of thumb is to use the largest model your hardware can run without skipping detections, which you can monitor on the <NavPath path="System > Metrics > Cameras" /> page in the UI. Better accuracy only helps if your detector keeps up with the detection load across all cameras.
**Variant size (tiny/small/medium):** Larger variants are gradually more accurate but slower. Whether the difference is noticeable depends on your specific cameras and scenes. A good rule of thumb is to use the largest model your hardware can run without skipping detections, which you can monitor on the <NavPath path="Health and Metrics > Cameras" /> page in the UI. Better accuracy only helps if your detector keeps up with the detection load across all cameras.
**Acceptable inference time depends on your hardware.** Inference time alone does not tell the whole story, because different hardware has different capacity. A GPU can run multiple instances of the same model concurrently, so an inference time around 30ms can still keep up with several cameras. A Google Coral runs only a single instance of the model, so it needs a much lower inference time (around 10ms) to keep up.