plus docs update

Since 0.18.2, every request that went through /auth logged an uninitialized "go2rtc_stream_proxy" variable warning, because only the go2rtc stream locations set that variable. The warning is now turned off in the /auth location, and the header value sent to /auth is unchanged.

docker/main/rootfs/usr/local/nginx/conf/auth_location.conf

@@ -5,6 +5,8 @@ location /auth {
     ## Essential Proxy Configuration
     internal;
     proxy_pass $upstream_auth;
+    # $go2rtc_stream_proxy is only set by the go2rtc stream locations
+    uninitialized_variable_warn off;
 
     ## Headers
 

docs/docs/configuration/object_detectors.md

@@ -87,7 +87,7 @@ Along with picking a detector for your hardware, you will choose a model's **inp
 
 :::tip
 
-The best detection accuracy comes from a model trained on images that look like what Frigate actually sees: security camera footage cropped to regions of interest. You can train or fine-tune your own model on images like this and run it as a custom model (see the per-detector sections below), but [Frigate+](/plus) makes this much easier by handling the training for you on images submitted from your own cameras. For YOLOv9, the `s` (small) variant at `320x320` resolution is a good place to start.
+The best detection accuracy comes from a model trained on images that look like what Frigate actually sees: security camera footage cropped to regions of interest. You can train or fine-tune your own model on images like this and run it as a custom model (see the per-detector sections below), but [Frigate+](/plus) makes this much easier by handling the training for you on images submitted from your own cameras. For YOLOv9, the `s` (small) variant at `320x320` resolution is a good place to start, and the larger `c` variant is available for more powerful hardware.
 
 :::
 

docs/docs/integrations/plus.md

@@ -59,7 +59,7 @@ You can view all of your submitted images at [https://plus.frigate.video](https:
 
 Once you have [requested your first model](../plus/first_model.md) and gotten your own model ID, it can be used with a special model path. No other information needs to be configured for Frigate+ models because it fetches the remaining config from Frigate+ automatically.
 
-You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
+You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+** tab), or manually set the model at the root level in your config:
 
 ```yaml
 detectors: ...

docs/docs/plus/annotating.md

@@ -25,6 +25,12 @@ If you have an active Frigate+ subscription, new uploads will be scanned for the
 
 Suggestions are converted to labels when saving, so you should remove any errant suggestions. There is already some logic designed to avoid duplicate labels, but you may still occasionally see some duplicate suggestions. You should keep the most accurate bounding box and delete any duplicates so that you have just one label per object remaining.
 
+## Reviewing annotations with Patches
+
+The Patches tab on the Images page shows a cropped view of every annotation for a label in a grid. Scanning the crops makes it much easier to spot a mislabeled object than paging through full images one at a time. Pick a label to get started, and optionally narrow the results to a single camera. Clicking a patch opens that image in the editor so you can fix the annotation.
+
+Patches only include annotations from images you have already verified.
+
 ## False positive labels
 
 False positives will be shown with a red box and the label will have a strike through. These can't be adjusted, but they can be deleted if you accidentally submit a true positive as a false positive from Frigate.
@@ -51,3 +57,12 @@ Misidentified objects should have a correct label added. For example, if a perso
 | `scrollwheel`     | Zoom in/out                   |
 | `f`               | Hide/show all but current box |
 | `spacebar`        | Verify and save               |
+
+## Deleting images
+
+In most cases, you should keep your existing images. Removing training data usually makes accuracy worse, not better, so you shouldn't delete images just because you think you made some annotation mistakes. Deleting images makes sense when you move to a new location, or significantly upgrade or reposition your cameras.
+
+- To delete all images for a camera, use the **Delete Images** button for that camera on the Cameras page.
+- To delete all images in your account, use **Delete All Images** on the Settings page.
+
+Deletions run in the background, and their progress is shown in the bulk operations panel. Deleted images cannot be recovered and are no longer used to train your models.

docs/docs/plus/faq.md

@@ -26,7 +26,9 @@ Yes. Subscriptions to Frigate+ provide access to the infrastructure used to trai
 ### Can I use Frigate+ models commercially?
 
 A standard subscription covers use on camera systems you own or operate, including for your business. A shop, restaurant, warehouse, or office running Frigate+ at its own locations (including multiple locations) is exactly the kind of use the subscription is for.
+
 What the standard subscription does not cover is using Frigate+ models to provide a product or service to others. If you're deploying models at your customers' sites, bundling them with hardware you sell, or running them as part of a hosted or managed service, even if your customers never receive the model files themselves, you'll need a commercial license.
+
 Note that professional installers are fine under standard subscriptions when each customer holds their own Frigate+ subscription. The commercial license is for cases where your license powers your customers' sites.
 
 ### Why can't I submit images to Frigate+?

docs/docs/plus/first_model.md

@@ -17,7 +17,7 @@ For more detailed recommendations, you can refer to the docs on [annotating](./a
 
 ## Step 2: Submit a model request
 
-Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). If you are unsure which type to request, you can test the base model for each version from the "Base Models" tab. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
+Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). If you are unsure which type to request, you can test the base model for each version from the "Base Models" tab. For most hardware, `yolov9` is recommended; `mobiledet` is a legacy model type and will be removed in 2027. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
 ![Plus Models Page](/img/plus/plus-models.jpg)
 
 ## Step 3: Set your model

docs/docs/plus/index.md

@@ -5,25 +5,31 @@ title: Models
 
 <a href="https://frigate.video/plus" target="_blank" rel="nofollow">Frigate+</a> offers models trained on images submitted by Frigate+ users from their security cameras and is specifically designed for the way Frigate NVR analyzes video footage. These models offer higher accuracy with less resources. The images you upload are used to fine tune a base model trained from images uploaded by all Frigate+ users. This fine tuning process results in a model that is optimized for accuracy in your specific conditions.
 
-With a subscription, 12 model trainings to fine tune your model per year are included. In addition, you will have access to any base models published while your subscription is active. If you cancel your subscription, you will retain access to any trained and base models in your account. An active subscription is required to submit model requests or purchase additional trainings. New base models are published quarterly with target dates of January 15th, April 15th, July 15th, and October 15th.
+With a subscription, 12 model trainings to fine tune your model per year are included. In addition, you will have access to any base models published while your subscription is active. If you cancel your subscription, you will retain access to any trained and base models in your account. An active subscription is required to submit model requests or purchase additional trainings. New base models are published twice per year.
 
 Information on how to integrate Frigate+ with Frigate can be found in the [integration docs](../integrations/plus.md).
 
 ## Available model types
 
-There are three model types offered in Frigate+, `mobiledet`, `yolonas`, and `yolov9`. All of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
+There are three model types offered in Frigate+, `yolov9`, `yolonas`, and `mobiledet` (legacy). All of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
 
 Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
 
 | Model Type  | Description                                                                                                                                                    |
 | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                              |
-| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                   |
 | `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on most hardware. |
+| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                   |
+| `mobiledet` | Legacy. Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                      |
+
+:::warning
+
+`mobiledet` is a legacy model type. Base model 2026.3 is the last base model update for `mobiledet`, and it is planned to be removed from Frigate+ in 2027. If you are using `mobiledet` with a Google Coral, request a `yolov9` model with the `edgetpu` variant instead.
+
+:::
 
 ### YOLOv9 Details
 
-YOLOv9 models are available in `s`, `t`, `edgetpu` variants. When requesting a `yolov9` model, you will be prompted to choose a variant. If you want the model to be compatible with a Google Coral, you will need to choose the `edgetpu` variant. If you are unsure what variant to choose, you should perform some tests with the base models to find the performance level that suits you. The `s` size is most similar to the current `yolonas` models in terms of inference times and accuracy, and a good place to start is the `320x320` resolution model for `yolov9s`.
+YOLOv9 models are available in `t`, `s`, `c`, and `edgetpu` variants, and an `m` size is coming soon. When requesting a `yolov9` model, you will be prompted to choose a variant. If you want the model to be compatible with a Google Coral, you will need to choose the `edgetpu` variant, which requires Frigate 0.17 or later. If you are unsure what variant to choose, you should perform some tests with the base models to find the performance level that suits you. The `s` size is most similar to the current `yolonas` models in terms of inference times and accuracy, and a good place to start is the `320x320` resolution model for `yolov9s`. The `c` size is larger and offers higher accuracy at the cost of slower inference, so it is best suited to more powerful hardware such as GPUs.
 
 :::info
 
@@ -33,7 +39,7 @@ When switching to YOLOv9, you may need to adjust your thresholds for some object
 
 #### Hailo Support
 
-If you have a Hailo device, you will need to specify the hardware you have when submitting a model request because they are not cross compatible. Please test using the available base models before submitting your model request.
+If you have a Hailo device, you will need to specify the hardware you have when submitting a model request because they are not cross compatible. Hailo models are available for the `t`, `s`, and `c` variants on Hailo8, Hailo8L, and Hailo8R devices. Please test using the available base models before submitting your model request.
 
 #### Rockchip (RKNN) Support
 
@@ -45,7 +51,7 @@ Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVi
 
 | Hardware                                                                         | Recommended Detector Type | Recommended Model Type |
 | -------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
-| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)           | `cpu`                     | `mobiledet`            |
+| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)           | `cpu`                     | `yolov9`               |
 | [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu`                 | `yolov9`               |
 | [Intel](/configuration/object_detectors.md#openvino-detector)                    | `openvino`                | `yolov9`               |
 | [NVidia GPU](/configuration/object_detectors#onnx)                               | `onnx`                    | `yolov9`               |
@@ -66,7 +72,7 @@ Some users may find that Frigate+ models result in more false positives initiall
 Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
 
 - **People**: `person`, `face`, `baby`
-- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage_truck`, `license_plate`
 - **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
 - **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`, `possum`, `rodent`
 - **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
@@ -81,7 +87,7 @@ Where possible, these labels are mapped to existing labels during training. For
 
 - **Vehicles**: `tractor`, `golf_cart`, `bus`, `airplane`, `helicopter`, `rickshaw`, `scooter`
 - **Delivery Logos**: `bpost`, `auspost`, `aramex`, `transoflex`, `parcelforce`, `hermes`, `cargus`, `fan_courier`, `sameday`, `la_poste`
-- **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`
+- **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`, `chipmunk`
 - **Other**: `sports_ball`, `drone`, `lawnmower`
 
 Candidate labels are not available for automatic suggestions.

docs/docs/troubleshooting/faqs.md

@@ -162,7 +162,7 @@ Open one of the tracked objects in Explore → **Tracking Details**. If the dete
 What helps:
 
 - **Improve the view**: even a small angle change that gets more of the car visible could lift scores enough to stabilize tracking.
-- **Use a more accurate model**: switching from `mobiledet` to `yolov9`, or stepping up to a larger variant like `yolov9-s` over `yolov9-t`, can help (at the cost of inference time, and still on the COCO dataset). The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. [Frigate+](https://frigate.video/plus) is a paid option that does this - models are trained on security-camera footage and can be fine-tuned on images you submit from your own setup.
+- **Use a more accurate model**: switching from `mobiledet` to `yolov9`, or stepping up to a larger variant like `yolov9-s` over `yolov9-t` (or `yolov9-c` on more powerful hardware), can help (at the cost of inference time, and still on the COCO dataset). The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. [Frigate+](https://frigate.video/plus) is a paid option that does this - models are trained on security-camera footage and can be fine-tuned on images you submit from your own setup.
 - **Don't set `detect -> stationary -> max_frames` for `car`**: it artificially ends tracking and forces re-detection as a new object. See [Stationary Objects](../configuration/stationary_objects.md).
 - **Restrict alerts to the areas you care about** with `required_zones`. See [Zones](../configuration/zones.md#restricting-alerts-and-detections-to-specific-zones). Make sure those zones use the default `loitering_time: 0` unless you specifically want the review item to stay open until the car leaves.
 - **Filter impossible locations** with [object filter masks](../configuration/masks.md#object-filter-masks) if cars are being detected on rooftops, treetops, etc.