Docs tweaks (#18025)

* face recognition usage instructions

* clarify lpr docs for motorcycles

* person must be detected before face

* add note about coral

* add note about local

* update reference config for face model size

* clarify reference config for face

docs/docs/configuration/face_recognition.md

@@ -3,7 +3,7 @@ id: face_recognition
 title: Face Recognition
 ---
 
-Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known person is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
+Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
 
 ## Model Requirements
 
@@ -13,6 +13,12 @@ When running a Frigate+ model (or any custom model that natively detects faces)
 
 When running a default COCO model or another model that does not include `face` as a detectable label, face detection will run via CV2 using a lightweight DNN model that runs on the CPU. In this case, you should _not_ define `face` in your list of objects to track.
 
+:::note
+
+Frigate needs to first detect a `person` before it can detect and recognize a face.
+
+:::
+
 ### Face Recognition
 
 Frigate has support for two face recognition model types:
@@ -22,6 +28,8 @@ Frigate has support for two face recognition model types:
 
 In both cases, a lightweight face landmark detection model is also used to align faces before running recognition.
 
+All of these features run locally on your system.
+
 ## Minimum System Requirements
 
 The `small` model is optimized for efficiency and runs on the CPU, most CPUs should run the model efficiently.
@@ -62,6 +70,13 @@ Fine-tune face recognition with these optional parameters:
 - `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
   - Default: `True`.
 
+## Usage
+
+1. **Enable face recognition** in your configuration file and restart Frigate.
+2. **Upload your face** using the **Add Face** button's wizard in the Face Library section of the Frigate UI.
+3. When Frigate detects and attempts to recognize a face, it will appear in the **Train** tab of the Face Library, along with its associated recognition confidence.
+4. From the **Train** tab, you can **assign the face** to a new or existing person to improve recognition accuracy for the future.
+
 ## Creating a Robust Training Set
 
 The number of images needed for a sufficient training set for face recognition varies depending on several factors:

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -24,3 +24,9 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
   - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
 
 Utilizing a GPU for enrichments does not require you to use the same GPU for object detection. For example, you can run the `tensorrt` Docker image for enrichments and still use other dedicated hardware for object detection.
+
+:::note
+
+A Google Coral is a TPU (Tensor Processing Unit), not a dedicated GPU (Graphics Processing Unit) and therefore does not provide any kind of acceleration for Frigate's enrichments.
+
+:::

docs/docs/configuration/license_plate_recognition.md

@@ -3,7 +3,7 @@ id: license_plate_recognition
 title: License Plate Recognition (LPR)
 ---
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a known name as a `sub_label` to tracked objects of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a known name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. However, LPR does not run on stationary vehicles.
 
@@ -13,7 +13,7 @@ When a plate is recognized, the details are:
 - Viewable in the Review Item Details pane in Review (sub labels).
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` (known) or `recognized_license_plate` (unknown) for the `car` tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` (known) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
 - Published via the `frigate/tracked_object_update` MQTT topic with `name` (if known) and `plate`.
 
 ## Model Requirements
@@ -24,7 +24,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
 
 :::note
 
-In the default mode, Frigate's LPR needs to first detect a `car` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
 
 :::
 
@@ -51,7 +51,7 @@ cameras:
       enabled: False
 ```
 
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car`, and that a car is actually being detected by Frigate. Otherwise, LPR will not run.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
 
 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
 
@@ -87,7 +87,7 @@ Fine-tune the LPR feature using these optional parameters at the global level of
 
 ### Matching
 
-- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` objects when a recognized plate matches a known value.
+- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` and `motorcycle` objects when a recognized plate matches a known value.
   - These labels appear in the UI, filters, and notifications.
   - Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
@@ -217,7 +217,7 @@ With this setup:
 - Snapshots will have license plate bounding boxes on them.
 - The `frigate/events` MQTT topic will publish tracked object updates.
 - Debug view will display `license_plate` bounding boxes.
-- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
 
 ### Using the Secondary LPR Pipeline (Without Frigate+)
 
@@ -311,9 +311,9 @@ Recognized plates will show as object labels in the debug view and will appear i
 
 If you are still having issues detecting plates, start with a basic configuration and see the debugging tips below.
 
-### Can I run LPR without detecting `car` objects?
+### Can I run LPR without detecting `car` or `motorcycle` objects?
 
-In normal LPR mode, Frigate requires a `car` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
 
 ### How can I improve detection accuracy?
 
@@ -336,8 +336,8 @@ Use `match_distance` to allow small character mismatches. Alternatively, define
 ### How do I debug LPR issues?
 
 - View MQTT messages for `frigate/events` to verify detected plates.
-- If you are using a Frigate+ model or a model that detects license plates, watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected with a `car`.
-- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` label will change to the recognized plate when LPR is enabled and working.
+- If you are using a Frigate+ model or a model that detects license plates, watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected with a `car` or `motorcycle`.
+- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
 - Adjust `detection_threshold` and `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 - Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
 - Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only enable this when necessary.
@@ -357,11 +357,11 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
 
 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
 
-If you are detecting `car` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
 
 ### It looks like Frigate picked up my camera's timestamp as the license plate. How can I prevent this?
 
-This could happen if cars travel close to your camera's timestamp. You could either move the timestamp through your camera's firmware, or apply a mask to it in Frigate.
+This could happen if cars or motorcycles travel close to your camera's timestamp. You could either move the timestamp through your camera's firmware, or apply a mask to it in Frigate.
 
 If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your timestamp.
 

docs/docs/configuration/reference.md

@@ -543,9 +543,9 @@ semantic_search:
   model_size: "small"
 
 # Optional: Configuration for face recognition capability
-# NOTE: Can (enabled, min_area) be overridden at the camera level
+# NOTE: enabled, min_area can be overridden at the camera level
 face_recognition:
-  # Optional: Enable semantic search (default: shown below)
+  # Optional: Enable face recognition (default: shown below)
   enabled: False
   # Optional: Minimum face distance score required to mark as a potential match (default: shown below)
   unknown_score: 0.8
@@ -560,6 +560,8 @@ face_recognition:
   save_attempts: 100
   # Optional: Apply a blur quality filter to adjust confidence based on the blur level of the image (default: shown below)
   blur_confidence_filter: True
+  # Optional: Set the model size used face recognition. (default: shown below)
+  model_size: small
 
 # Optional: Configuration for license plate recognition capability
 # NOTE: enabled, min_area, and enhancement can be overridden at the camera level