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Miscellaneous fixes (0.17 beta) (#21683)
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Browse Source 
* misc triggers tweaks

i18n fixes
fix toaster color
fix clicking on labels selecting incorrect checkbox

* update copilot instructions

* lpr docs tweaks

* add retry params to gemini

* i18n fix

* ensure users only see recognized plates from accessible cameras in explore

* ensure all zone filters are converted to pixels

zone-level filters were never converted from percentage area to pixels. RuntimeFilterConfig was only applied to filters at the camera level, not zone.filters.

Fixes https://github.com/blakeblackshear/frigate/discussions/21694

* add test for percentage based zone filters

* use export id for key instead of name

* update gemini docs
This commit is contained in:
Josh Hawkins
2026-01-18 06:36:27 -07:00
committed by GitHub
parent cfeb86646f
commit 0a8f499640
12 changed files with 105 additions and 29 deletions
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docs/docs/configuration/license_plate_recognition.md
+3 -3
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@@ -68,8 +68,8 @@ Fine-tune the LPR feature using these optional parameters at the global level of
- Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
- Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
- **`device`**: Device to use to run license plate detection _and_ recognition models.
- Default: `CPU`
- This can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
- Default: `None`
- This is auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
- **`model_size`**: The size of the model used to identify regions of text on plates.
- Default: `small`
- This can be `small` or `large`.
@@ -432,6 +432,6 @@ If you are using a model that natively detects `license_plate`, add an _object m
If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
### I see "Error running ... model" in my logs. How can I fix this?
### I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?
This usually happens when your GPU is unable to compile or use one of the LPR models. Set your `device` to `CPU` and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.