Merge e888dc347a into a0d6cb5c15

* fix config examples

* remove reference to trt model generation script

* tweak tmpfs comment

* update old version

* tweak tmpfs comment

* clean up and clarify tensorrt

* re-add size

* Update docs/docs/configuration/hardware_acceleration_enrichments.md

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

docs/docs/configuration/face_recognition.md

@@ -5,6 +5,18 @@ 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.
 
+## Alerts and Notifications
+
+Face recognition does not affect whether an alert is created — alerts are based on tracked objects like `person` in your `review.alerts.labels` and your [zone requirements](./review). The `face` label is an [attribute label](/plus/#available-label-types), not a tracked object, so it cannot trigger alerts on its own.
+
+When a face is recognized, the person's name is added as a `sub_label` on the tracked object. This name appears in the Frigate UI, in [built-in notifications](/configuration/notifications), and is published via [MQTT](/integrations/mqtt).
+
+:::note
+
+There is no built-in way to only create alerts for specific recognized faces. Neither `face`, `person-verified`, nor specific person names can be used in `review.alerts.labels`. To trigger automations based on face recognition results, use the [official Frigate integration's sensors](/integrations/home-assistant) and/or the [MQTT data](/integrations/mqtt) Frigate publishes.
+
+:::
+
 ## Model Requirements
 
 ### Face Detection
@@ -69,9 +81,9 @@ Fine-tune face recognition with these optional parameters at the global level of
   - Default: `0.9`.
 - `min_faces`: Min face recognitions for the sub label to be applied to the person object.
   - Default: `1`
-- `save_attempts`: Number of images of recognized faces to save for training.
+- `save_attempts`: Maximum number of face attempt images to keep in the training folder. Frigate saves a face image after each recognition attempt; when the limit is reached, the oldest image is deleted. These images are displayed in the Face Library's Recent Recognitions tab.
   - Default: `200`.
-- `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
+- `blur_confidence_filter`: Enables a filter that measures face image blurriness (using Laplacian variance) and reduces the recognition confidence score accordingly. Blurrier images receive a larger penalty (up to -0.06 for very blurry, down to 0 for clear images), making it harder for blurry faces to meet the `recognition_threshold`.
   - Default: `True`.
 - `device`: Target a specific device to run the face recognition model on (multi-GPU installation).
   - Default: `None`.
@@ -118,9 +130,19 @@ When choosing images to include in the face training set it is recommended to al
 
 The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
 
-Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
+Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. The score is color-coded based on your configured thresholds:
 
-Refer to the guidelines below for best practices on selecting images for training.
+- **Green**: score >= `recognition_threshold` (default `0.9`) — a confident match
+- **Orange**: score >= `unknown_score` (default `0.8`) — a potential match
+- **Red**: score < `unknown_score` — unknown or no match
+
+When an event has multiple recognition attempts, the face cards are displayed within a group. The group shows the recognized person's name if one was identified, or "Unknown" if not. Within the group, each individual face card shows its own recognition score. Frigate uses a weighted average across all attempts for a person object to determine whether to assign a name (`sub_label`) — so a single high-scoring card does not guarantee the person will be identified (see the [FAQ](#i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned) for more details).
+
+If the weighted average did not meet the `recognition_threshold`, there is no place in the UI to see it. The weighted average is published in the `score` field of the [`frigate/tracked_object_update`](/integrations/mqtt.md#face-recognition-update) MQTT topic after each recognition attempt, regardless of whether it meets the threshold. This is the most useful tool for debugging why a sub label was or wasn't assigned.
+
+Clicking a face card navigates to the Tracked Object Details for the associated event. To select face cards for deletion, right-click (or Ctrl/Cmd+click) individual cards, or use Ctrl+A to select all. A delete button will appear in the toolbar once cards are selected. Removing cards from the Recent Recognitions tab only removes the saved attempt images — it does not affect recognition accuracy or training data.
+
+While each image can be used to train the system for a specific person, not all images are suitable for training. Refer to the guidelines below for best practices on selecting images for training.
 
 ### Step 1 - Building a Strong Foundation
 
@@ -157,6 +179,8 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
    - Make sure you have trained at least one face per the recommendations above.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 
+3. To see recognition scores for an event, check the **Face Library** > **Recent Recognitions** tab. Face cards from the same event are grouped together, with the group header showing the combined result. Each card within the group shows its individual recognition score with [color coding](#understanding-the-recent-recognitions-tab). The **Tracked Object Details** view only shows the final weighted average score (in parentheses next to the top score) if a `sub_label` was assigned.
+
 ### Detection does not work well with blurry images?
 
 Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
@@ -190,7 +214,15 @@ For more guidance, refer to the section above on improving recognition accuracy.
 
 ### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?
 
-The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
+Frigate considers recognition scores across all attempts for each person object. The score shown in the UI is the final weighted average across all attempts, while MQTT publishes a running weighted average that updates after each attempt. The weighting favors larger faces (by pixel area, capped at 4000px) and higher-confidence detections. Attempts scored at or below `unknown_score` are excluded from the average.
+
+A sub label will only be assigned if:
+
+- At least `min_faces` recognition attempts have been recorded.
+- A single person name has the most detections (no ties).
+- The weighted average score meets the `recognition_threshold`.
+
+This avoids cases where a single high-confidence recognition would throw off the results.
 
 ### Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?
 

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -12,23 +12,20 @@ Some of Frigate's enrichments can use a discrete GPU or integrated GPU for accel
 Object detection and enrichments (like Semantic Search, Face Recognition, and License Plate Recognition) are independent features. To use a GPU / NPU for object detection, see the [Object Detectors](/configuration/object_detectors.md) documentation. If you want to use your GPU for any supported enrichments, you must choose the appropriate Frigate Docker image for your GPU / NPU and configure the enrichment according to its specific documentation.
 
 - **AMD**
-
   - ROCm support in the `-rocm` Frigate image is automatically detected for enrichments, but only some enrichment models are available due to ROCm's focus on LLMs and limited stability with certain neural network models. Frigate disables models that perform poorly or are unstable to ensure reliable operation, so only compatible enrichments may be active.
 
 - **Intel**
-
   - OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
   - **Note:** Intel NPUs have limited model support for enrichments. GPU is recommended for enrichments when available.
 
 - **Nvidia**
-
   - Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
   - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
 
 - **RockChip**
   - RockChip NPU will automatically be detected and used for semantic search v1 and face recognition in the `-rk` 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 like a Coral or Hailo for object detection. However, one combination that is not supported is TensorRT for object detection and OpenVINO for enrichments.
+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 to run enrichments on an Nvidia GPU and still use other dedicated hardware like a Coral or Hailo for object detection. However, one combination that is not supported is the `tensorrt` image for object detection on an Nvidia GPU and Intel iGPU for enrichments.
 
 :::note
 

docs/docs/configuration/index.md

@@ -29,12 +29,12 @@ cameras:
 
 When running Frigate through the HA Add-on, the Frigate `/config` directory is mapped to `/addon_configs/<addon_directory>` in the host, where `<addon_directory>` is specific to the variant of the Frigate Add-on you are running.
 
-| Add-on Variant             | Configuration directory                      |
-| -------------------------- | -------------------------------------------- |
-| Frigate                    | `/addon_configs/ccab4aaf_frigate`            |
-| Frigate (Full Access)      | `/addon_configs/ccab4aaf_frigate-fa`         |
-| Frigate Beta               | `/addon_configs/ccab4aaf_frigate-beta`       |
-| Frigate Beta (Full Access) | `/addon_configs/ccab4aaf_frigate-fa-beta`    |
+| Add-on Variant             | Configuration directory                   |
+| -------------------------- | ----------------------------------------- |
+| Frigate                    | `/addon_configs/ccab4aaf_frigate`         |
+| Frigate (Full Access)      | `/addon_configs/ccab4aaf_frigate-fa`      |
+| Frigate Beta               | `/addon_configs/ccab4aaf_frigate-beta`    |
+| Frigate Beta (Full Access) | `/addon_configs/ccab4aaf_frigate-fa-beta` |
 
 **Whenever you see `/config` in the documentation, it refers to this directory.**
 
@@ -109,15 +109,16 @@ detectors:
 
 record:
   enabled: True
-  retain:
+  motion:
     days: 7
-    mode: motion
   alerts:
     retain:
       days: 30
+      mode: motion
   detections:
     retain:
       days: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -165,15 +166,16 @@ detectors:
 
 record:
   enabled: True
-  retain:
+  motion:
     days: 7
-    mode: motion
   alerts:
     retain:
       days: 30
+      mode: motion
   detections:
     retain:
       days: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -231,15 +233,16 @@ model:
 
 record:
   enabled: True
-  retain:
+  motion:
     days: 7
-    mode: motion
   alerts:
     retain:
       days: 30
+      mode: motion
   detections:
     retain:
       days: 30
+      mode: motion
 
 snapshots:
   enabled: True

docs/docs/configuration/notifications.md

@@ -49,7 +49,7 @@ Once notifications are enabled, press the `Register for Notifications` button on
 
 ## Supported Notifications
 
-Currently notifications are only supported for review alerts. More notifications will be supported in the future.
+Currently notifications are only supported for review alerts. More notifications will be supported in the future. If [face recognition](/configuration/face_recognition) identifies a person during the alert, their name will be included in the notification. However, built-in notifications cannot be configured to only fire for specific recognized faces.
 
 :::note
 

docs/docs/configuration/object_detectors.md

@@ -34,7 +34,7 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Nvidia GPU**
 
-- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
+- [ONNX](#onnx): Nvidia GPUs will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
 
 **Nvidia Jetson** <CommunityBadge />
 
@@ -65,7 +65,7 @@ This does not affect using hardware for accelerating other tasks such as [semant
 
 # Officially Supported Detectors
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `memryx`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
 
 ## Edge TPU Detector
 
@@ -654,11 +654,9 @@ ONNX is an open format for building machine learning models, Frigate supports ru
 If the correct build is used for your GPU then the GPU will be detected and used automatically.
 
 - **AMD**
-
   - ROCm will automatically be detected and used with the ONNX detector in the `-rocm` Frigate image.
 
 - **Intel**
-
   - OpenVINO will automatically be detected and used with the ONNX detector in the default Frigate image.
 
 - **Nvidia**

docs/docs/frigate/hardware.md

@@ -41,8 +41,8 @@ If the EQ13 is out of stock, the link below may take you to a suggested alternat
 | Name                                                                                                          | Capabilities                                                               | Notes                                               |
 | ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------- |
 | Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | Can run object detection on several 1080p cameras with low-medium activity | Dual gigabit NICs for easy isolated camera network. |
-| Intel 1120p ([Amazon](https://www.amazon.com/Beelink-i3-1220P-Computer-Display-Gigabit/dp/B0DDCKT9YP)         | Can handle a large number of 1080p cameras with high activity              |                                                     |
-| Intel 125H ([Amazon](https://www.amazon.com/MINISFORUM-Pro-125H-Barebone-Computer-HDMI2-1/dp/B0FH21FSZM)      | Can handle a significant number of 1080p cameras with high activity        | Includes NPU for more efficient detection in 0.17+  |
+| Intel 1120p ([Amazon](https://www.amazon.com/Beelink-i3-1220P-Computer-Display-Gigabit/dp/B0DDCKT9YP))        | Can handle a large number of 1080p cameras with high activity              |                                                     |
+| Intel 125H ([Amazon](https://www.amazon.com/MINISFORUM-Pro-125H-Barebone-Computer-HDMI2-1/dp/B0FH21FSZM))     | Can handle a significant number of 1080p cameras with high activity        | Includes NPU for more efficient detection in 0.17+  |
 
 ## Detectors
 
@@ -86,7 +86,7 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Nvidia**
 
-- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs to provide efficient object detection.
+- [Nvidia GPU](#nvidia-gpus): Nvidia GPUs can provide efficient object detection.
   - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
   - Runs well with any size models including large
 
@@ -172,7 +172,7 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp
 | Intel Arc A380 | ~ 6 ms                     |                                                   | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms  |                                    |
 | Intel Arc A750 | ~ 4 ms                     |                                                   | 320: ~ 8 ms               |                        |                                    |
 
-### TensorRT - Nvidia GPU
+### Nvidia GPUs
 
 Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA libraries.
 
@@ -182,8 +182,6 @@ Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA
 
 Make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
 
-There are improved capabilities in newer GPU architectures that TensorRT can benefit from, such as INT8 operations and Tensor cores. The features compatible with your hardware will be optimized when the model is converted to a trt file. Currently the script provided for generating the model provides a switch to enable/disable FP16 operations. If you wish to use newer features such as INT8 optimization, more work is required.
-
 #### Compatibility References:
 
 [NVIDIA TensorRT Support Matrix](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/getting-started/support-matrix.html)
@@ -192,7 +190,7 @@ There are improved capabilities in newer GPU architectures that TensorRT can ben
 
 [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus)
 
-Inference speeds will vary greatly depending on the GPU and the model used.
+Inference is done with the `onnx` detector type. Speeds will vary greatly depending on the GPU and the model used.
 `tiny (t)` variants are faster than the equivalent non-tiny model, some known examples are below:
 
 ✅ - Accelerated with CUDA Graphs

docs/docs/frigate/installation.md

@@ -56,7 +56,7 @@ services:
     volumes:
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # Recommended: 1GB of memory
+      - type: tmpfs # 1GB In-memory filesystem for recording segment storage
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -123,7 +123,7 @@ On Raspberry Pi OS **Trixie**, the Hailo driver is no longer shipped with the ke
    :::note
 
    If you are **not** using a Raspberry Pi with **Bookworm OS**, skip this step and proceed directly to step 2.
-   
+
    If you are using Raspberry Pi with **Trixie OS**, also skip this step and proceed directly to step 2.
 
    :::
@@ -133,13 +133,13 @@ On Raspberry Pi OS **Trixie**, the Hailo driver is no longer shipped with the ke
    ```bash
    lsmod | grep hailo
    ```
-   
+
    If it shows `hailo_pci`, unload it:
 
    ```bash
    sudo modprobe -r hailo_pci
    ```
-   
+
    Then locate the built-in kernel driver and rename it so it cannot be loaded.
    Renaming allows the original driver to be restored later if needed.
    First, locate the currently installed kernel module:
@@ -149,28 +149,29 @@ On Raspberry Pi OS **Trixie**, the Hailo driver is no longer shipped with the ke
    ```
 
    Example output:
-   
+
    ```
    /lib/modules/6.6.31+rpt-rpi-2712/kernel/drivers/media/pci/hailo/hailo_pci.ko.xz
    ```
+
    Save the module path to a variable:
-   
+
    ```bash
    BUILTIN=$(modinfo -n hailo_pci)
    ```
 
    And rename the module by appending .bak:
-    
+
    ```bash
    sudo mv "$BUILTIN" "${BUILTIN}.bak"
    ```
-   
+
    Now refresh the kernel module map so the system recognizes the change:
-   
+
    ```bash
    sudo depmod -a
    ```
-   
+
    Reboot your Raspberry Pi:
 
    ```bash
@@ -206,7 +207,6 @@ On Raspberry Pi OS **Trixie**, the Hailo driver is no longer shipped with the ke
    ```
 
    The script will:
-
    - Install necessary build dependencies
    - Clone and build the Hailo driver from the official repository
    - Install the driver
@@ -236,18 +236,18 @@ On Raspberry Pi OS **Trixie**, the Hailo driver is no longer shipped with the ke
    ```
 
    Verify the driver version:
-   
+
    ```bash
    cat /sys/module/hailo_pci/version
    ```
-   
+
    Verify that the firmware was installed correctly:
-   
+
    ```bash
    ls -l /lib/firmware/hailo/hailo8_fw.bin
    ```
 
-  **Optional: Fix PCIe descriptor page size error**
+   **Optional: Fix PCIe descriptor page size error**
 
    If you encounter the following error:
 
@@ -462,7 +462,7 @@ services:
       - /etc/localtime:/etc/localtime:ro
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # Recommended: 1GB of memory
+      - type: tmpfs # 1GB In-memory filesystem for recording segment storage
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -502,12 +502,12 @@ The official docker image tags for the current stable version are:
 
 - `stable` - Standard Frigate build for amd64 & RPi Optimized Frigate build for arm64. This build includes support for Hailo devices as well.
 - `stable-standard-arm64` - Standard Frigate build for arm64
-- `stable-tensorrt` - Frigate build specific for amd64 devices running an nvidia GPU
+- `stable-tensorrt` - Frigate build specific for amd64 devices running an Nvidia GPU
 - `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
 
 The community supported docker image tags for the current stable version are:
 
-- `stable-tensorrt-jp6` - Frigate build optimized for nvidia Jetson devices running Jetpack 6
+- `stable-tensorrt-jp6` - Frigate build optimized for Nvidia Jetson devices running Jetpack 6
 - `stable-rk` - Frigate build for SBCs with Rockchip SoC
 
 ## Home Assistant Add-on
@@ -521,7 +521,7 @@ There are important limitations in HA OS to be aware of:
 - Separate local storage for media is not yet supported by Home Assistant
 - AMD GPUs are not supported because HA OS does not include the mesa driver.
 - Intel NPUs are not supported because HA OS does not include the NPU firmware.
-- Nvidia GPUs are not supported because addons do not support the nvidia runtime.
+- Nvidia GPUs are not supported because addons do not support the Nvidia runtime.
 
 :::
 
@@ -694,17 +694,18 @@ Log into QNAP, open Container Station. Frigate docker container should be listed
 
 :::warning
 
-macOS uses port 5000 for its Airplay Receiver service.  If you want to expose port 5000 in Frigate for local app and API access the port will need to be mapped to another port on the host e.g. 5001
+macOS uses port 5000 for its Airplay Receiver service. If you want to expose port 5000 in Frigate for local app and API access the port will need to be mapped to another port on the host e.g. 5001
 
 Failure to remap port 5000 on the host will result in the WebUI and all API endpoints on port 5000 being unreachable, even if port 5000 is exposed correctly in Docker.
 
 :::
 
-Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application. 
+Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
 
 To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
 
 #### Docker Compose example
+
 ```yaml
 services:
   frigate:
@@ -719,7 +720,7 @@ services:
     ports:
       - "8971:8971"
       # If exposing on macOS map to a diffent host port like 5001 or any orher port with no conflicts
-      # - "5001:5000" # Internal unauthenticated access. Expose carefully. 
+      # - "5001:5000" # Internal unauthenticated access. Expose carefully.
       - "8554:8554" # RTSP feeds
     extra_hosts:
       # This is very important

docs/docs/frigate/updating.md

@@ -20,7 +20,6 @@ Keeping Frigate up to date ensures you benefit from the latest features, perform
 If you’re running Frigate via Docker (recommended method), follow these steps:
 
 1. **Stop the Container**:
-
    - If using Docker Compose:
      ```bash
      docker compose down frigate
@@ -31,9 +30,8 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
      ```
 
 2. **Update and Pull the Latest Image**:
-
    - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.17.0` instead of `0.16.3`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.17.0` instead of `0.16.4`). For example:
        ```yaml
        services:
          frigate:
@@ -51,7 +49,6 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
        ```
 
 3. **Start the Container**:
-
    - If using Docker Compose:
      ```bash
      docker compose up -d
@@ -75,18 +72,15 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
 For users running Frigate as a Home Assistant Addon:
 
 1. **Check for Updates**:
-
    - Navigate to **Settings > Add-ons** in Home Assistant.
    - Find your installed Frigate addon (e.g., "Frigate NVR" or "Frigate NVR (Full Access)").
    - If an update is available, you’ll see an "Update" button.
 
 2. **Update the Addon**:
-
    - Click the "Update" button next to the Frigate addon.
    - Wait for the process to complete. Home Assistant will handle downloading and installing the new version.
 
 3. **Restart the Addon**:
-
    - After updating, go to the addon’s page and click "Restart" to apply the changes.
 
 4. **Verify the Update**:
@@ -105,8 +99,8 @@ If an update causes issues:
 1. Stop Frigate.
 2. Restore your backed-up config file and database.
 3. Revert to the previous image version:
-   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.3`) in your `docker run` command.
-   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.3`), and re-run `docker compose up -d`.
+   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.4`) in your `docker run` command.
+   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.4`), and re-run `docker compose up -d`.
    - For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
 4. Verify the old version is running again.
 

docs/docs/guides/getting_started.md

@@ -119,7 +119,7 @@ services:
     volumes:
       - ./config:/config
       - ./storage:/media/frigate
-      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
+      - type: tmpfs # 1GB In-memory filesystem for recording segment storage
         target: /tmp/cache
         tmpfs:
           size: 1000000000