add model fetcher endpoint for genai config ui

fix admin response cache leak to non-admin users via nginx proxy_cache
remove camera ui section in settings
2026-07-20 18:59:01 +03:00 · 2026-05-20 08:47:49 -05:00 · 2026-05-20 07:05:48 -05:00 · 2026-05-19 18:46:54 -05:00 · 2026-05-19 18:38:57 -05:00 · 2026-05-19 18:25:20 -05:00
890 changed files with 10510 additions and 41098 deletions
@@ -162,7 +162,6 @@ mpegts
 mqtt
 mse
 msenc
-muxing
 namedtuples
 nbytes
 nchw
@@ -198,8 +197,6 @@ OWASP
 paddleocr
 paho
 passwordless
-PCMA
-PCMU
 popleft
 posthog
 postprocess
@@ -225,9 +222,7 @@ radeontop
 rawvideo
 rcond
 RDONLY
-realmonitor
 rebranded
-recvonly
 referer
 reindex
 Reolink
@@ -244,11 +239,8 @@ rocminfo
 rootfs
 rtmp
 RTSP
-rtsps
-rtspx
 ruamel
 scroller
-sendonly
 setproctitle
 setpts
 shms
@@ -259,7 +251,6 @@ SNDMORE
 socs
 sqliteq
 sqlitevecq
-Srtp
 ssdlite
 statm
 stimeout
@@ -10,8 +10,7 @@ body:

        Before submitting, read the [beta documentation][docs].

-        [docs]: https://docs-dev.frigate.video/
-        [discussions]: https://github.com/blakeblackshear/frigate/discussions
+        [docs]: https://deploy-preview-19787--frigate-docs.netlify.app/
  - type: textarea
    id: description
    attributes:
@@ -23,8 +22,8 @@ body:
    id: version
    attributes:
      label: Beta Version
-      description: Visible on the System Metrics page in the Web UI. Please include the full version including the build identifier (eg. 0.18.0-beta1, 0.18.0-8b72c7a, etc.)
-      placeholder: "0.18.0-beta1"
+      description: Visible on the System page in the Web UI. Please include the full version including the build identifier (eg. 0.17.0-beta1)
+      placeholder: "0.17.0-beta1"
    validations:
      required: true
  - type: dropdown
@@ -72,12 +71,11 @@ body:
    attributes:
      label: Install method
      options:
-        - Home Assistant App
+        - Home Assistant Add-on
        - Docker Compose
        - Docker CLI
        - Proxmox via Docker
-        - Proxmox via installation script
-        - Proxomox via VM
+        - Proxmox via TTeck Script
        - Windows WSL2
    validations:
      required: true
@@ -87,12 +87,11 @@ body:
    attributes:
      label: Install method
      options:
-        - Home Assistant App
+        - Home Assistant Add-on
        - Docker Compose
        - Docker CLI
        - Proxmox via Docker
-        - Proxmox via installation script
-        - Proxomox via VM
+        - Proxmox via TTeck Script
        - Windows WSL2
    validations:
      required: true
@@ -73,12 +73,11 @@ body:
    attributes:
      label: Install method
      options:
-        - Home Assistant App
+        - Home Assistant Add-on
        - Docker Compose
        - Docker CLI
        - Proxmox via Docker
-        - Proxmox via installation script
-        - Proxomox via VM
+        - Proxmox via TTeck Script
        - Windows WSL2
    validations:
      required: true
@@ -53,12 +53,11 @@ body:
    attributes:
      label: Install method
      options:
-        - Home Assistant App
+        - Home Assistant Add-on
        - Docker Compose
        - Docker CLI
        - Proxmox via Docker
-        - Proxmox via installation script
-        - Proxomox via VM
+        - Proxmox via TTeck Script
        - Windows WSL2
    validations:
      required: true
@@ -73,12 +73,11 @@ body:
    attributes:
      label: Install method
      options:
-        - Home Assistant App
+        - Home Assistant Add-on
        - Docker Compose
        - Docker CLI
        - Proxmox via Docker
-        - Proxmox via installation script
-        - Proxmox via VM
+        - Proxmox via TTeck Script
        - Windows WSL2
    validations:
      required: true
@@ -69,12 +69,11 @@ body:
    attributes:
      label: Install method
      options:
-        - Home Assistant App
+        - Home Assistant Add-on
        - Docker Compose
        - Docker CLI
        - Proxmox via Docker
-        - Proxmox via installation script
-        - Proxomox via VM
+        - Proxmox via TTeck Script
        - Windows WSL2
    validations:
      required: true
@@ -6,7 +6,7 @@ body:
      value: |
        Use this form to submit a reproducible bug in Frigate or Frigate's UI.

-        **⚠️ If you are running a beta version (0.18.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
+        **⚠️ If you are running a beta version (0.17.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**

        Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.

@@ -116,13 +116,9 @@ body:
    attributes:
      label: Install method
      options:
-        - Home Assistant App
+        - Home Assistant Add-on
        - Docker Compose
        - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via installation script
-        - Proxomox via VM
-        - Windows WSL2
    validations:
      required: true
  - type: dropdown
@@ -125,7 +125,5 @@ jobs:
        run: devcontainer up --workspace-folder .
      - name: Run mypy in devcontainer
        run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m mypy --config-file frigate/mypy.ini frigate"
-      - name: Check API spec is up to date
-        run: devcontainer exec --workspace-folder . bash -lc "python3 generate_api_auth_spec.py --check"
      - name: Run unit tests in devcontainer
        run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m unittest"
@@ -38,7 +38,6 @@ When reviewing code, do NOT comment on:
 - **Type Checking**: Use type hints consistently
 - **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
 - **Language**: American English for all code, comments, and documentation
- **Punctuation**: Do not use em dashes in documentation, comments, or strings; reword with standard punctuation (commas, colons, parentheses, or separate sentences)

 ### Logging Standards

@@ -236,14 +235,6 @@ ruff check frigate/

 # Type check
 python3 -u -m mypy --config-file frigate/mypy.ini frigate
-
-# Regenerate the OpenAPI spec after adding, changing, or removing an API
-# endpoint or its auth dependency — outputs docs/static/frigate-api.yaml,
-# annotated with each endpoint's auth requirement (admin / any / camera /
-# public). NEVER edit that file by hand. CI runs the --check variant and fails
-# if it is out of date. (from repo root)
-python3 generate_api_auth_spec.py
-python3 generate_api_auth_spec.py --check
 ```

 ### Frontend (from web/ directory)
@@ -325,8 +316,6 @@ async def get_events(request: Request, limit: int = 100):
    # Implementation
 ```

-After adding, changing, or removing an endpoint (or its auth dependency), regenerate the OpenAPI spec with `python3 generate_api_auth_spec.py` so `docs/static/frigate-api.yaml` stays in sync and the endpoint's auth requirement is documented. CI enforces this via the `--check` variant; never edit that file by hand.
-
 ### Configuration Access

 ```python
@@ -63,7 +63,7 @@ RUN --mount=type=bind,source=docker/main/build_intel_media_driver.sh,target=/dep
 FROM scratch AS go2rtc
 ARG TARGETARCH
 WORKDIR /rootfs/usr/local/go2rtc/bin
-ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.14/go2rtc_linux_${TARGETARCH}" go2rtc
+ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.13/go2rtc_linux_${TARGETARCH}" go2rtc

 FROM wget AS tempio
 ARG TARGETARCH
@@ -265,8 +265,8 @@ ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PA
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
    /deps/install_deps.sh

-ENV DEFAULT_FFMPEG_VERSION="8.0"
-ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:7.0:5.0"
+ENV DEFAULT_FFMPEG_VERSION="7.0"
+ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:5.0"

 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
    && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
@@ -52,13 +52,9 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
    rm -rf ffmpeg.tar.xz
    mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linux64-gpl-7.1.tar.xz"
    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
    rm -rf ffmpeg.tar.xz
-    mkdir -p /usr/lib/ffmpeg/8.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linux64-gpl-8.1.tar.xz"
-    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/8.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
-    rm -rf ffmpeg.tar.xz
 fi

 # ffmpeg -> arm64
@@ -68,13 +64,9 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
    rm -f ffmpeg.tar.xz
    mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linuxarm64-gpl-7.1.tar.xz"
    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
    rm -f ffmpeg.tar.xz
-    mkdir -p /usr/lib/ffmpeg/8.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linuxarm64-gpl-8.1.tar.xz"
-    tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/8.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
-    rm -f ffmpeg.tar.xz
 fi

 # arch specific packages
@@ -1,4 +1,4 @@
-ruff == 0.15.20
+ruff

 # types
 types-peewee == 3.17.*
@@ -5,7 +5,11 @@ from typing import Any
 from ruamel.yaml import YAML

 sys.path.insert(0, "/opt/frigate")
-from frigate.util.config import find_config_file, resolve_ffmpeg_path
+from frigate.const import (
+    DEFAULT_FFMPEG_VERSION,
+    INCLUDED_FFMPEG_VERSIONS,
+)
+from frigate.util.config import find_config_file

 sys.path.remove("/opt/frigate")

@@ -25,4 +29,9 @@ except FileNotFoundError:
    config: dict[str, Any] = {}

 path = config.get("ffmpeg", {}).get("path", "default")
-print(resolve_ffmpeg_path(path, "ffmpeg"))
+if path == "default":
+    print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg")
+elif path in INCLUDED_FFMPEG_VERSIONS:
+    print(f"/usr/lib/ffmpeg/{path}/bin/ffmpeg")
+else:
+    print(f"{path}/bin/ffmpeg")
@@ -3,7 +3,6 @@
 import json
 import os
 import sys
-from pathlib import Path
 from typing import Any

 from ruamel.yaml import YAML
@@ -12,28 +11,18 @@ sys.path.insert(0, "/opt/frigate")
 from frigate.config.env import substitute_frigate_vars
 from frigate.const import (
    BIRDSEYE_PIPE,
+    DEFAULT_FFMPEG_VERSION,
+    INCLUDED_FFMPEG_VERSIONS,
    LIBAVFORMAT_VERSION_MAJOR,
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
-from frigate.util.config import find_config_file, resolve_ffmpeg_path
-from frigate.util.services import (
-    is_go2rtc_arbitrary_exec_allowed,
-    is_restricted_go2rtc_source,
-)
+from frigate.util.config import find_config_file
+from frigate.util.services import is_restricted_go2rtc_source

 sys.path.remove("/opt/frigate")

 yaml = YAML()

-FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
-# read docker secret files as env vars too
-if os.path.isdir("/run/secrets"):
-    for secret_file in os.listdir("/run/secrets"):
-        if secret_file.startswith("FRIGATE_"):
-            FRIGATE_ENV_VARS[secret_file] = (
-                Path(os.path.join("/run/secrets", secret_file)).read_text().strip()
-            )
-
 config_file = find_config_file()

 try:
@@ -92,7 +81,12 @@ if go2rtc_config.get("rtsp", {}).get("password") is not None:

 # ensure ffmpeg path is set correctly
 path = config.get("ffmpeg", {}).get("path", "default")
-ffmpeg_path = resolve_ffmpeg_path(path, "ffmpeg")
+if path == "default":
+    ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
+elif path in INCLUDED_FFMPEG_VERSIONS:
+    ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
+else:
+    ffmpeg_path = f"{path}/bin/ffmpeg"

 if go2rtc_config.get("ffmpeg") is None:
    go2rtc_config["ffmpeg"] = {"bin": ffmpeg_path}
@@ -113,7 +107,7 @@ for name in list(go2rtc_config.get("streams", {})):

    if isinstance(stream, str):
        try:
-            formatted_stream = stream.format(**FRIGATE_ENV_VARS)
+            formatted_stream = substitute_frigate_vars(stream)
            if is_restricted_go2rtc_source(formatted_stream):
                print(
                    f"[ERROR] Stream '{name}' uses a restricted source (echo/expr/exec) which is disabled by default for security. "
@@ -132,7 +126,7 @@ for name in list(go2rtc_config.get("streams", {})):
        filtered_streams = []
        for i, stream_item in enumerate(stream):
            try:
-                formatted_stream = stream_item.format(**FRIGATE_ENV_VARS)
+                formatted_stream = substitute_frigate_vars(stream_item)
                if is_restricted_go2rtc_source(formatted_stream):
                    print(
                        f"[ERROR] Stream '{name}' item {i + 1} uses a restricted source (echo/expr/exec) which is disabled by default for security. "
@@ -156,20 +150,6 @@ for name in list(go2rtc_config.get("streams", {})):
            )
            del go2rtc_config["streams"][name]

-    elif isinstance(stream, dict):
-        # The map form ({"url": ...}) lets go2rtc resolve the source
-        # recursively, so it is effectively a dynamic way to generate the URL
-        # for a stream. That can only be backed by an exec source, so it cannot
-        # be allowed unless arbitrary exec is explicitly enabled. When it is
-        # enabled, leave the map untouched for go2rtc to resolve.
-        if not is_go2rtc_arbitrary_exec_allowed():
-            print(
-                f"[ERROR] Stream '{name}' uses a dynamic source format which is disabled by default for security. "
-                f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
-            )
-            del go2rtc_config["streams"][name]
-            continue
-
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
    birdseye: dict[str, Any] = config.get("birdseye")
@@ -11,10 +11,10 @@ except FileNotFoundError:
    pass

 try:
-    with open("/config/conv2rknn.yaml") as config_file:
+    with open("/config/conv2rknn.yaml", "r") as config_file:
        configuration = yaml.safe_load(config_file)
 except FileNotFoundError:
-    raise Exception("Please place a config file at /config/conv2rknn.yaml") from None
+    raise Exception("Please place a config file at /config/conv2rknn.yaml")

 if configuration["config"] != None:
    rknn_config = configuration["config"]
@@ -31,7 +31,7 @@ if "soc" not in configuration:
        with open("/proc/device-tree/compatible") as file:
            soc = file.read().split(",")[-1].strip("\x00")
    except FileNotFoundError:
-        raise Exception("Make sure to run docker in privileged mode.") from None
+        raise Exception("Make sure to run docker in privileged mode.")

    configuration["soc"] = [
        soc,
@@ -1,6 +1,7 @@
 ---
-id: system
-title: System
+id: advanced
+title: Advanced Options
+sidebar_label: Advanced Options
 ---

 import ConfigTabs from "@site/src/components/ConfigTabs";
@@ -72,10 +73,10 @@ Variables prefixed with `FRIGATE_` can be referenced in config fields that suppo

 Navigate to <NavPath path="Settings > System > Environment variables" /> to add or edit environment variables.

-| Field             | Description                                               |
-| ----------------- | --------------------------------------------------------- |
-| **Variable name** | The environment variable name (e.g., `FRIGATE_MQTT_USER`) |
-| **Value**         | The value for the variable                                |
+| Field     | Description                                               |
+| --------- | --------------------------------------------------------- |
+| **Key**   | The environment variable name (e.g., `FRIGATE_MQTT_USER`) |
+| **Value** | The value for the variable                                |

 Variables defined here can be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.

@@ -201,7 +202,7 @@ model:

 :::warning

-If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../review.md#restricting-alerts-to-specific-labels) for more info.
+If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../configuration/review.md#restricting-alerts-to-specific-labels) for more info.

 :::

@@ -233,16 +234,26 @@ Some labels have special handling and modifications can disable functionality.

 ## Network Configuration

-Frigate exposes a few networking options. IPv6 and the listen ports are set in the `networking` configuration (or from the Settings UI); more advanced changes require [customizing the bundled Nginx configuration](#customizing-the-nginx-configuration).
+Changes to Frigate's internal network configuration can be made by bind mounting nginx.conf into the container. For example:
+
+```yaml
+services:
+  frigate:
+    container_name: frigate
+    ...
+    volumes:
+      ...
+      - /path/to/your/nginx.conf:/usr/local/nginx/conf/nginx.conf
+```

 ### Enabling IPv6

-By default Frigate listens on IPv4 only. To also listen on IPv6 (on port `5000`, and on `8971` when TLS is configured), enable it in the `networking` configuration.
+IPv6 is disabled by default. Enable it in the Frigate configuration.

 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > System > Networking" /> and enable **IPv6**.
+Navigate to <NavPath path="Settings > System > Networking" /> and expand **IPv6 configuration**, then enable **Enable IPv6**.

 </TabItem>
 <TabItem value="yaml">
@@ -250,7 +261,7 @@ Navigate to <NavPath path="Settings > System > Networking" /> and enable **IPv6*
 ```yaml
 networking:
  ipv6:
-    enabled: true
+    enabled: True
 ```

 </TabItem>
@@ -289,20 +300,6 @@ This setting is for advanced users. For the majority of use cases it's recommend

 :::

-### Customizing the Nginx configuration
-
-More advanced changes to Frigate's internal network configuration can be made by bind mounting your own `nginx.conf` into the container. For example:
-
-```yaml
-services:
-  frigate:
-    container_name: frigate
-    ...
-    volumes:
-      ...
-      - /path/to/your/nginx.conf:/usr/local/nginx/conf/nginx.conf
-```
-
 ## Base path

 By default, Frigate runs at the root path (`/`). However some setups require to run Frigate under a custom path prefix (e.g. `/frigate`), especially when Frigate is located behind a reverse proxy that requires path-based routing.
@@ -354,7 +351,7 @@ To do this:

 ### Custom go2rtc version

-Frigate currently includes go2rtc v1.9.14, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.13, there may be certain cases where you want to run a different version of go2rtc.

 To do this:

@@ -54,7 +54,7 @@ The ffmpeg process for capturing audio will be a separate connection to the came
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add an input with the `audio` role pointing to a stream that includes audio.
+Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add an input with the `audio` role pointing to a stream that includes audio.

 </TabItem>
 <TabItem value="yaml">
@@ -78,7 +78,7 @@ cameras:

 ### Configuring Minimum Volume

-The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The [Debug view](/usage/live#the-single-camera-view) in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
+The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The Debug view in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are is displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.

 :::tip

@@ -88,7 +88,7 @@ Volume is considered motion for recordings, this means when the `record -> retai

 ### Configuring Audio Events

-The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `speech`, and `yell` are enabled but these can be customized.
+The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `scream`, `speech`, and `yell` are enabled but these can be customized.

 <ConfigTabs>
 <TabItem value="ui">
@@ -107,6 +107,7 @@ audio:
  listen:
    - bark
    - fire_alarm
+    - scream
    - speech
    - yell
 ```
@@ -114,73 +115,9 @@ audio:
 </TabItem>
 </ConfigTabs>

-### Common Audio Labels
-
-The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.
-
-Some labels cover several related sounds: `yell` is triggered by shouting, yelling, children shouting, and screaming; `crying` covers baby cries, sobbing, and whimpering; and `speech` covers ordinary talking and conversation.
-
-**Safety and security**
-
-| Label            | Detects                            |
-| ---------------- | ---------------------------------- |
-| `yell`           | Shouting, yelling, screaming       |
-| `fire_alarm`     | Fire and smoke alarm sirens        |
-| `smoke_detector` | Smoke detector beeps               |
-| `alarm`          | General alarm sounds               |
-| `car_alarm`      | Car alarms                         |
-| `siren`          | Emergency vehicle and civil sirens |
-| `glass`          | Glass clinking                     |
-| `shatter`        | Breaking glass                     |
-| `breaking`       | Something breaking                 |
-| `gunshot`        | Gunshots                           |
-| `explosion`      | Explosions                         |
-
-**People and activity**
-
-| Label       | Detects                  |
-| ----------- | ------------------------ |
-| `speech`    | Talking and conversation |
-| `laughter`  | Laughing                 |
-| `crying`    | Baby crying and sobbing  |
-| `cough`     | Coughing                 |
-| `footsteps` | Footsteps and walking    |
-| `knock`     | Knocking on a door       |
-| `doorbell`  | Doorbell                 |
-| `ding-dong` | Doorbell chime           |
-
-**Pets and animals**
-
-| Label      | Detects          |
-| ---------- | ---------------- |
-| `bark`     | Dog barking      |
-| `dog`      | Other dog sounds |
-| `howl`     | Howling          |
-| `growling` | Growling         |
-| `meow`     | Cat meowing      |
-| `cat`      | Other cat sounds |
-| `hiss`     | Hissing          |
-
-**Vehicles and driveway**
-
-| Label             | Detects              |
-| ----------------- | -------------------- |
-| `car`             | Passing cars         |
-| `honk`            | Car horns            |
-| `truck`           | Trucks               |
-| `reversing_beeps` | Vehicle backup beeps |
-| `motorcycle`      | Motorcycles          |
-| `engine_starting` | Engines starting     |
-
-:::tip
-
-Frequently-heard labels like `speech` can generate a lot of events, and each event could save a snapshot and recording based on your configuration, so start with a focused set and expand from there. The defaults (`bark`, `fire_alarm`, `speech`, `yell`) plus a few of the safety labels above cover most needs. See the [full audio labelmap](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) or the Frigate UI for every available type.
-
-:::
-
 ### Audio Transcription

-Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
+Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.

 :::info

@@ -91,7 +91,7 @@ auth:

 ## Session Length

-The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required. Otherwise, the user will need to log in again.
+The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required — otherwise, the user will need to log in again.

 While the default provides a balance of security and convenience, you can customize this duration to suit your specific security requirements and user experience preferences. The session length is configured in seconds.

@@ -141,7 +141,7 @@ Changing the secret will invalidate current tokens.

 ## Proxy configuration

-Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth. Frigate does not implement OIDC, SAML, or LDAP natively; as an NVR focused on recording and object detection, it relies on robust, battle-tested proxies to handle those protocols and passes the authenticated user and role through via headers (see below).
+Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth.

 If you are leveraging the authentication of an upstream proxy, you likely want to disable Frigate's authentication as there is no correspondence between users in Frigate's database and users authenticated via the proxy. Optionally, if communication between the reverse proxy and Frigate is over an untrusted network, you should set an `auth_secret` in the `proxy` config and configure the proxy to send the secret value as a header named `X-Proxy-Secret`. Assuming this is an untrusted network, you will also want to [configure a real TLS certificate](tls.md) to ensure the traffic can't simply be sniffed to steal the secret.

@@ -6,7 +6,6 @@ title: Camera Autotracking
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
-import FaqItem from "@site/src/components/FaqItem";

 An ONVIF-capable, PTZ (pan-tilt-zoom) camera that supports relative movement within the field of view (FOV) can be configured to automatically track moving objects and keep them in the center of the frame.

@@ -162,13 +161,13 @@ Every PTZ camera is different, so autotracking may not perform ideally in every

 The object tracker in Frigate estimates the motion of the PTZ so that tracked objects are preserved when the camera moves. In most cases 5 fps is sufficient, but if you plan to track faster moving objects, you may want to increase this slightly. Higher frame rates (> 10fps) will only slow down Frigate and the motion estimator and may lead to dropped frames, especially if you are using experimental zooming.

-A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's [debug viewer](/usage/live#the-single-camera-view) for your camera to see a thicker colored box around the object currently being autotracked.
+A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's debug viewer for your camera to see a thicker colored box around the object currently being autotracked.

 ![Autotracking Debug View](/img/autotracking-debug.gif)

 A full-frame zone in `required_zones` is not recommended, especially if you've calibrated your camera and there are `movement_weights` defined in the configuration file. Frigate will continue to autotrack an object that has entered one of the `required_zones`, even if it moves outside of that zone.

-Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](advanced/reference.md).
+Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](index.md).

 ## Zooming

@@ -188,96 +187,30 @@ In security and surveillance, it's common to use "spotter" cameras in combinatio

 ## Troubleshooting and FAQ

-### Camera Compatibility
-
-<FaqItem id="which-ptz-camera-should-i-use-for-autotracking" question="Which PTZ camera should I use for autotracking?">
-
-See the community-maintained list of [ONVIF PTZ camera recommendations](cameras.md#onvif-ptz-camera-recommendations) for cameras and brands reported to work (and not work) with autotracking. This is not an exhaustive list that is frequently updated, so other cameras not listed may also work well. Frigate's autotracking was developed with a Dahua SD1A404XB-GNR (now sold as the EmpireTech PTZ1A4M-4X-S2), and Dahua / EmpireTech PTZs are the most consistently reported as working well.
-
-When comparing models:
-
- Verify ONVIF support first. See [Checking ONVIF camera support](#checking-onvif-camera-support) above.
- Favor a camera with a fast PTZ motor. Cameras with slow motors may fail [calibration](#calibration) and will struggle to keep up with objects that move across the field of view quickly.
-
-</FaqItem>
-
-<FaqItem id="does-autotracking-work-with-reolink-ptz-cameras" question="Does autotracking work with Reolink PTZ cameras?">
-
-No. Reolink cameras (including the TrackMix series) lack the ONVIF FOV RelativeMove firmware support that Frigate's autotracker requires, so autotracking will not work with any current Reolink PTZ. Their video streams and basic PTZ controls still work in Frigate. If you want object tracking on a Reolink PTZ, you will need to use the tracking feature built into the camera's firmware, which is proprietary and operates independently of Frigate.
-
-</FaqItem>
-
-<FaqItem id="im-seeing-an-error-in-the-logs-that-my-camera-is-still-in-onvif-moving-status-what-does-this-mean" question={"I'm seeing an error in the logs that my camera \"is still in ONVIF 'MOVING' status.\" What does this mean?"}>
-
-There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware, such as some Annke models. In rare cases the vendor may provide fixed firmware on request; for example, Annke has supplied firmware that resolves this for the CZ504 (see the [camera recommendations list](cameras.md#onvif-ptz-camera-recommendations)).
-
-</FaqItem>
-
-<FaqItem id="calibration-seems-to-have-completed-but-the-camera-is-not-actually-moving-to-track-my-object-why" question="Calibration seems to have completed, but the camera is not actually moving to track my object. Why?">
-
-Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
-
-</FaqItem>
-
-### Calibration Issues
-
-<FaqItem id="i-tried-calibrating-my-camera-but-the-logs-show-that-it-is-stuck-at-0-and-frigate-is-not-starting-up" question="I tried calibrating my camera, but the logs show that it is stuck at 0% and Frigate is not starting up.">
-
-This is often caused by the same reason as the "MOVING" status error above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
-
-</FaqItem>
-
-<FaqItem id="frigate-reports-an-error-saying-that-calibration-has-failed-why" question="Frigate reports an error saying that calibration has failed. Why?">
-
-Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner.
-
-Some things to try:
-
- If your camera's firmware has a PTZ or motor speed setting, set it to the fastest available speed and calibrate again.
- Run without calibration: remove the `movement_weights` line from your config, set `calibrate_on_startup` to `False`, and restart.
-
-If calibration consistently fails, this often means your camera's motor is too slow and autotracking will behave unpredictably or won't be able to keep up with moving objects.
-
-</FaqItem>
-
-<FaqItem id="autotracking-is-erratic-or-moves-the-camera-in-the-wrong-direction" question="Autotracking is erratic, moves the camera in the wrong direction, or zooms past my object. Why?">
-
-Frigate uses the `movement_weights` measured during calibration to predict how far the camera needs to move to keep an object centered, so inaccurate values produce movements that don't seem to make sense: overshooting, moving the opposite direction, or zooming in on an object's last known position and losing it entirely. This is almost always a calibration issue.
-
- Remove the `movement_weights` entry from your config and restart Frigate to run without calibration. If tracking improves, try recalibrating.
- Recalibrate several times. The `movement_weights` values should be close to each other after each run. If they vary significantly between runs, your camera may not be reporting its motor status reliably, and you may get better results without calibration.
- If you are using zooming, a high `zoom_factor` can cause the camera to zoom in too far and lose the object. Try a lower value.
-
-Remember to recalibrate whenever you change your `return_preset`, change your camera's detect `fps`, or enable zooming after calibrating with it disabled.
-
-</FaqItem>
-
-### Tracking Behavior
-
-<FaqItem id="the-autotracker-loses-track-of-my-object-why" question="The autotracker loses track of my object. Why?">
+### The autotracker loses track of my object. Why?

 There are many reasons this could be the case. If you are using experimental zooming, your `zoom_factor` value might be too high, the object might be traveling too quickly, the scene might be too dark, there are not enough details in the scene (for example, a PTZ looking down on a driveway or other monotone background without a sufficient number of hard edges or corners), or the scene is otherwise less than optimal for Frigate to maintain tracking.

 Your camera's shutter speed may also be set too low so that blurring occurs with motion. Check your camera's firmware to see if you can increase the shutter speed.

-Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it. If the camera consistently zooms in on the object and then loses it, see [Autotracking is erratic, moves the camera in the wrong direction, or zooms past my object. Why?](#autotracking-is-erratic-or-moves-the-camera-in-the-wrong-direction) above.
+Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it.

-</FaqItem>
+### I'm seeing an error in the logs that my camera "is still in ONVIF 'MOVING' status." What does this mean?

-<FaqItem id="im-seeing-this-error-in-the-logs-autotracker-motion-estimator-couldnt-get-transformations-what-does-this-mean" question={"I'm seeing this error in the logs: \"Autotracker: motion estimator couldn't get transformations\". What does this mean?"}>
+There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware.
+
+### I tried calibrating my camera, but the logs show that it is stuck at 0% and Frigate is not starting up.
+
+This is often caused by the same reason as above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
+
+### I'm seeing this error in the logs: "Autotracker: motion estimator couldn't get transformations". What does this mean?

 To maintain object tracking during PTZ moves, Frigate tracks the motion of your camera based on the details of the frame. If you are seeing this message, it could mean that your `zoom_factor` may be set too high, the scene around your detected object does not have enough details (like hard edges or color variations), or your camera's shutter speed is too slow and motion blur is occurring. Try reducing `zoom_factor`, finding a way to alter the scene around your object, or changing your camera's shutter speed.

-</FaqItem>
+### Calibration seems to have completed, but the camera is not actually moving to track my object. Why?

-<FaqItem id="why-does-object-detection-pause-briefly-when-the-camera-moves" question="Why does object detection pause briefly when the camera moves?">
+Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.

-When the PTZ moves, the entire frame changes at once. Frigate's motion detection treats sudden scene-wide changes (like a lightning flash, an infrared mode switch, or a camera move) specially and pauses detection momentarily until the scene stabilizes. This is expected and normal, and detection resumes shortly after the camera stops moving. If detection does not resume once the camera is stationary, use the [debug view](/usage/live#the-single-camera-view) to see what is happening.
+### Frigate reports an error saying that calibration has failed. Why?

-</FaqItem>
-
-<FaqItem id="can-i-turn-autotracking-on-and-off-automatically" question="Can I turn autotracking on and off automatically?">
-
-Yes. Autotracking can be toggled per camera at runtime over MQTT with the [`frigate/<camera_name>/ptz_autotracker/set`](../integrations/mqtt.md#frigatecamera_nameptz_autotrackerset) topic, and the [Home Assistant integration](../integrations/home-assistant.md) exposes a switch for it. This pairs well with the "spotter" camera automations described in [Usage applications](#usage-applications) above, for example only enabling autotracking at night or when nobody is home.
-
-</FaqItem>
+Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner. You can try running without calibration (just remove the `movement_weights` line from your config and restart), but if calibration fails, this often means that autotracking will behave unpredictably.
@@ -6,16 +6,10 @@ import NavPath from "@site/src/components/NavPath";

 In addition to Frigate's Live camera dashboard, Birdseye allows a portable heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about.

-Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the pencil icon in the sidebar on the Live page, and choose "Birdseye" as one of the cameras.
+Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the "+" icon on the Live page, and choose "Birdseye" as one of the cameras.

 Birdseye can also be used in Home Assistant dashboards, cast to media devices, etc.

-:::note
-
-Each camera tile in Birdseye is composed from the frames of the stream assigned the `detect` role, so a camera's image quality in Birdseye matches its detect stream resolution rather than a higher-resolution recording stream. If a camera looks low quality in Birdseye, increasing the detect width and height (or assigning the `detect` role to a higher-resolution stream) is what affects it. See [setting up camera inputs](./cameras.md#setting-up-camera-inputs) for how roles are assigned.
-
-:::
-
 ## Birdseye Behavior

 ### Birdseye Modes
@@ -41,10 +35,10 @@ To include a camera in Birdseye view only for specific circumstances, or exclude

 **Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.

-| Field               | Description                                                   |
-| ------------------- | ------------------------------------------------------------- |
-| **Enable Birdseye** | Whether this camera appears in Birdseye view                  |
-| **Tracking mode**   | When to show the camera: `continuous`, `motion`, or `objects` |
+| Field | Description |
+|-------|-------------|
+| **Enable Birdseye** | Whether this camera appears in Birdseye view |
+| **Tracking mode** | When to show the camera: `continuous`, `motion`, or `objects` |

 </TabItem>
 <TabItem value="yaml">
@@ -78,8 +72,8 @@ By default birdseye shows all cameras that have had the configured activity in t

 Navigate to <NavPath path="Settings > System > Birdseye" />.

-| Field                    | Description                                                                 |
-| ------------------------ | --------------------------------------------------------------------------- |
+| Field | Description |
+|-------|-------------|
 | **Inactivity threshold** | Seconds of inactivity before a camera is hidden from Birdseye (default: 30) |

 </TabItem>
@@ -106,9 +100,9 @@ The resolution and aspect ratio of birdseye can be configured. Resolution will i

 Navigate to <NavPath path="Settings > System > Birdseye" />.

-| Field      | Description                                     |
-| ---------- | ----------------------------------------------- |
-| **Width**  | Birdseye output width in pixels (default: 1280) |
+| Field | Description |
+|-------|-------------|
+| **Width** | Birdseye output width in pixels (default: 1280) |
 | **Height** | Birdseye output height in pixels (default: 720) |

 </TabItem>
@@ -126,12 +120,12 @@ birdseye:

 ### Sorting cameras in the Birdseye view

-It is possible to override the order of cameras that are being shown in the Birdseye view. The order is set at the camera level (when using YAML).
+It is possible to override the order of cameras that are being shown in the Birdseye view. The order is set at the camera level.

 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > System > Birdseye" /> and in the **Camera order** field, use the drag handle next to each camera name to control the display order.
+Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> for each camera and set the **Position** field to control the display order.

 </TabItem>
 <TabItem value="yaml">
@@ -167,8 +161,8 @@ It is possible to limit the number of cameras shown on birdseye at one time. Whe

 Navigate to <NavPath path="Settings > System > Birdseye" />.

-| Field                    | Description                                                                         |
-| ------------------------ | ----------------------------------------------------------------------------------- |
+| Field | Description |
+|-------|-------------|
 | **Layout > Max cameras** | Maximum number of cameras shown at once (e.g., `1` for only the most active camera) |

 </TabItem>
@@ -193,8 +187,8 @@ By default birdseye tries to fit 2 cameras in each row and then double in size u

 Navigate to <NavPath path="Settings > System > Birdseye" />.

-| Field                       | Description                                              |
-| --------------------------- | -------------------------------------------------------- |
+| Field | Description |
+|-------|-------------|
 | **Layout > Scaling factor** | Camera scaling factor between 1.0 and 5.0 (default: 2.0) |

 </TabItem>
@@ -3,8 +3,6 @@ id: camera_specific
 title: Camera Specific Configurations
 ---

-import NavPath from "@site/src/components/NavPath";
-
 :::note

 This page makes use of presets of FFmpeg args. For more information on presets, see the [FFmpeg Presets](/configuration/ffmpeg_presets) page.
@@ -150,34 +148,19 @@ WEB Digest Algorithm  - MD5

 Reolink has many different camera models with inconsistently supported features and behavior. The below table shows a summary of various features and recommendations.

-| Camera Resolution | Camera Generation         | Recommended Stream Type           | Additional Notes                                                                            |
-| ----------------- | ------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------- |
-| 5MP or lower      | All                       | http-flv                          | Stream is h264                                                                              |
-| 6MP or higher     | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 (Frigate's default) |
-| 6MP or higher     | Older (ex: RLC-8##)       | rtsp                              |                                                                                             |
+| Camera Resolution | Camera Generation         | Recommended Stream Type           | Additional Notes                                                        |
+| ----------------- | ------------------------- | --------------------------------- | ----------------------------------------------------------------------- |
+| 5MP or lower      | All                       | http-flv                          | Stream is h264                                                          |
+| 6MP or higher     | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 |
+| 6MP or higher     | Older (ex: RLC-8##)       | rtsp                              |                                                                         |

-Frigate works much better with newer Reolink cameras that are setup with the below options:
+Frigate works much better with newer reolink cameras that are setup with the below options:

 If available, recommended settings are:

 - `On, fluency first` this sets the camera to CBR (constant bit rate)
 - `Interframe Space 1x` this sets the iframe interval to the same as the frame rate

-#### Setup via the Add Camera Wizard
-
-The Add Camera Wizard is the recommended way to add a standard Reolink camera. Before starting, make sure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. The wizard uses the camera's HTTP API to determine its resolution and choose the recommended stream type from the table above.
-
-1. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />.
-2. Choose **Manual selection** as the stream detection method and select **Reolink** as the camera brand.
-3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower, or an RTSP stream for higher resolution cameras.
-4. In the validation step, enable **Use stream compatibility mode** for http-flv streams when the wizard recommends it.
-
-If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras where http-flv is recommended, the wizard will show a warning in the validation step.
-
-The wizard covers standard single-camera setups. For two way talk, cameras connected through a Reolink NVR, or audio transcoding for WebRTC live view, configure the camera manually as shown below.
-
-#### Manual configuration
-
 According to [this discussion](https://github.com/blakeblackshear/frigate/issues/3235#issuecomment-1135876973), the http video streams seem to be the most reliable for Reolink.

 Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
@@ -192,7 +175,7 @@ Reolink's latest cameras support two way audio via go2rtc and other applications

 NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.

-Ensure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
+Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).

 :::

@@ -204,7 +187,7 @@ go2rtc:
      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
    your_reolink_camera_sub:
      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
-    # example for connecting to a Reolink camera that supports two way talk
+    # example for connectin to a Reolink camera that supports two way talk
    your_reolink_camera_twt:
      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
      - "rtsp://username:password@reolink_ip/Preview_01_sub"
@@ -242,14 +225,13 @@ cameras:
          roles:
            - detect
 ```
-
 </details>

 ### Unifi Protect Cameras

-:::note
+:::note 

-Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not possible to enable it in standalone mode.
+Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not posible to enable it in standalone mode.

 :::

@@ -264,7 +246,7 @@ go2rtc:
      - rtspx://192.168.1.1:7441/abcdefghijk
 ```

-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-rtsp)

 In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.

@@ -287,6 +269,7 @@ Some community members have found better performance on Wyze cameras by using an
 To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:

 - Preparation outside of Frigate:
+
  - Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
  - Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
  - If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
@@ -24,14 +24,12 @@ Each role can only be assigned to one input per camera. The options for roles ar
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 | Field             | Description                                                         |
 | ----------------- | ------------------------------------------------------------------- |
 | **Camera inputs** | List of input stream definitions (paths and roles) for this camera. |

-For each input you can choose its source: select **Restream (go2rtc)** to pick an existing [go2rtc stream](restream.md) from a dropdown (Frigate uses the `rtsp://127.0.0.1:8554/<stream>` path and `preset-rtsp-restream` input args for that input automatically), or **Manual input path** to type the stream URL directly.
-
 Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.

 | Field             | Description                                                                                            |
@@ -69,7 +67,7 @@ Additional cameras are simply added under the camera configuration section.
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and use the add camera button to configure each additional camera.
+Navigate to <NavPath path="Settings > Camera configuration > Management" /> and use the add camera button to configure each additional camera.

 </TabItem>
 <TabItem value="yaml">
@@ -145,11 +143,6 @@ If your ONVIF camera does not require authentication credentials, you may still

 :::

-If a camera connects but fails to authenticate, two optional fields can help:
-
- `tls_insecure`: Skips TLS certificate verification and sends the ONVIF password as plaintext (`PasswordText`) instead of a hashed digest (`PasswordDigest`). Some cameras reject the digest token and only accept plaintext. This weakens connection security, so only enable it on a trusted local network.
- `ignore_time_mismatch`: ONVIF authentication tokens include a timestamp, and a camera will reject the token if its clock differs too much from Frigate's. Enabling this makes Frigate compensate for the time offset so authentication can still succeed. Running NTP on both the camera and the Frigate host is the recommended fix; only use this in a "safe" environment, as it slightly weakens token validation.
-
 If your camera has multiple ONVIF profiles, you can specify which one to use for PTZ control with the `profile` option, matched by token or name. When not set, Frigate selects the first profile with a valid PTZ configuration. Check the Frigate debug logs (`frigate.ptz.onvif: debug`) to see available profile names and tokens for your camera.

 An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
@@ -181,7 +174,7 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
 | Hikvision DS-2DE3A404IWG-E/W |      ✅      |      ✅      |                                                                                                                                                                                                                                                                   |
 | Reolink                      |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Speco O8P32X                 |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
-| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatible.                                                                                                                                                              |
+| Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable.                                                                                                                                                              |
 | Tapo                         |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                                                                                                                                   |
 | Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                                                                                                                                    |
 | Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                                                                                                                                             |
@@ -194,7 +187,7 @@ Camera groups let you organize cameras together with a shared name and icon, mak
 <ConfigTabs>
 <TabItem value="ui">

-On the Live dashboard, press the **pencil icon** in the main navigation to add a new camera group. Configure the group name, select which cameras to include, choose an icon, and set the display order.
+On the Live dashboard, press the **+** icon in the main navigation to add a new camera group. Configure the group name, select which cameras to include, choose an icon, and set the display order.

 </TabItem>
 <TabItem value="yaml">
@@ -137,7 +137,7 @@ If examples for some of your classes do not appear in the grid, you can continue

 :::tip Diversity matters far more than volume

-Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the class. **This is why Frigate does not implement bulk training in the UI.**
+Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data — the model learns what _that exact moment_ looked like rather than what actually defines the class. **This is why Frigate does not implement bulk training in the UI.**

 For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).

@@ -149,16 +149,9 @@ For more detail, see [Frigate Tip: Best Practices for Training Face and Custom C
 - **The wizard is just the starting point**: You don't need to find and label every class upfront. Missing classes will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
 - **Preprocessing**: Ensure examples reflect object crops similar to Frigate's boxes; keep the subject centered.
- **Crop size**: Aim for crops of at least 100×100 pixels (a 10,000 pixel area). Crops smaller than ~80×80 get stretched 3-7× by the model's 224×224 input resize and tend to collapse into a generic "blob" region of feature space where identity becomes unreliable. If most of your detections are small because the camera is far from the subject, consider repositioning the camera for closer crops.
- **Class balance**: Aim to keep your largest class within ~3× the count of your smallest. Beyond that, the model becomes biased toward the dominant class and tends to default borderline predictions to it (the "everything looks like Buddy" failure mode).
+- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
 - **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.

-:::tip `none` works differently from named classes
-
-Named classes work best with visually uniform examples. Every Buddy photo should look like Buddy. The `none` class needs the opposite: visual diversity across sizes, framings, and qualities, because at inference it has to absorb everything that isn't one of your named classes. Don't apply the same "only keep large, well-framed images" rule to `none` that you would to a named class. Mix in small crops, partial views, and false positives deliberately - otherwise the model has no signal for "small/ambiguous thing = not one of my known classes" and will force those crops into a named class by default.
-
-:::
-
 ## Debugging Classification Models

 To troubleshoot issues with object classification models, enable debug logging to see detailed information about classification attempts, scores, and consensus calculations.
@@ -103,7 +103,7 @@ Once some images are assigned, training will begin automatically.

 :::tip Diversity matters far more than volume

-Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
+Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data — the model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**

 For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).

@@ -6,7 +6,6 @@ title: Face Recognition
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
-import FaqItem from "@site/src/components/FaqItem";

 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.

@@ -87,7 +86,7 @@ Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
 - **Detection threshold**: Face detection confidence score required before recognition runs. This field only applies to the standalone face detection model; `min_score` should be used to filter for models that have face detection built in.
  - Default: `0.7`
 - **Minimum face area**: Minimum size (in pixels) a face must be before recognition runs. Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
-  - Default: `750` pixels
+  - Default: `500` pixels

 </TabItem>
 <TabItem value="yaml">
@@ -96,7 +95,7 @@ Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
 face_recognition:
  enabled: true
  detection_threshold: 0.7
-  min_area: 750
+  min_area: 500
 ```

 </TabItem>
@@ -152,14 +151,6 @@ Follow these steps to begin:

 ## Creating a Robust Training Set

-:::tip
-
-**The short version:** Start with a few clear, front-facing photos of each person. As faces are detected in the Recent Recognitions tab, train clear images that scored lower, adding variety (different angles, lighting, and expressions) slowly. Diversity matters far more than volume, and low-quality images hurt recognition more than they help.
-
-For a step-by-step narrative of these best practices (and the same principles applied to state and object classification), see the [Frigate Tips: Best Practices for Training](https://github.com/blakeblackshear/frigate/discussions/21374) discussion.
-
-:::
-
 The number of images needed for a sufficient training set for face recognition varies depending on several factors:

 - Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
@@ -190,27 +181,9 @@ 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 that recognition attempt. Images are grouped by the person they were matched against, not by who they actually are, so a group labeled with a person's name can contain a crop that is really someone else but happened to score as a partial match. The name and score shown on each individual crop describe that single attempt.
+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.

-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.
-
-### How Frigate Decides Who a Person Is
-
-Recognition does not happen one frame at a time. While a `person` is in view, Frigate runs face recognition on many frames, not just a single frame. The final `sub_label` is decided from all of those attempts together, weighted by the area of each face (larger, closer faces count more), not from any single frame.
-
-This has a few practical consequences:
-
- A handful of wrong guesses on blurry or distant frames usually do not change the result. If Frigate sees a person as "Tom, Tom, Sam, Tom, Tom," it will still conclude the person was Tom.
- The goal is not for every individual face crop to be correct. The goal is for each person to be recognized correctly overall, across all the faces captured while they were present.
- A single very high confidence match will not by itself assign a sub label. Recognition must be consistent. See [I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?](#i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned) below.
-
-### Which Faces Are Worth Training?
-
-Whether a face is worth training has little to do with what it was recognized as. A crop is a good training candidate when all of these are true:
-
- It did not already score high and correctly. Faces that are already recognized confidently add little and increase the risk of over-fitting.
- It is clear enough to be useful: not blurry, not heavily off-axis, not infrared (gray-scale). If it is hard for you to make out the face, it will not help the model.
- It adds something new: a different angle, lighting, expression, or distance than what you already have.
+Refer to the guidelines below for best practices on selecting images for training.

 ### Step 1 - Building a Strong Foundation

@@ -226,16 +199,14 @@ Once front-facing images are performing well, start choosing slightly off-angle

 ## FAQ

-### Getting Recognition Working
-
-<FaqItem id="how-do-i-debug-face-recognition-issues" question="How do I debug Face Recognition issues?">
+### How do I debug Face Recognition issues?

 Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.

 1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.

   If you are using a Frigate+ or `face` detecting model:
-   - Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
+   - Watch the debug view (Settings --> Debug) to ensure that `face` is being detected along with `person`.
   - You may need to adjust the `min_score` for the `face` object if faces are not being detected.

   If you are **not** using a Frigate+ or `face` detecting model:
@@ -246,47 +217,21 @@ 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).

-</FaqItem>
+### Detection does not work well with blurry images?

-<FaqItem id="does-face-recognition-run-on-the-recording-stream" question="Does face recognition run on the recording stream?">
-
-Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
-
-1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
-2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
-3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
-
-</FaqItem>
-
-### Improving Accuracy and Training
-
-<FaqItem id="detection-does-not-work-well-with-blurry-images" question="Detection does not work well with blurry images?">
-
-Accuracy is definitely 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.
+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.

 Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).

-</FaqItem>
-
-<FaqItem id="can-i-train-faces-for-people-who-only-appear-at-night" question="Can I train faces for people who only appear at night?">
-
-The embedding models are trained on color images, so gray-scale and infrared (IR) faces sit in a different feature distribution and are more easily confused with other people. Prefer color images, and avoid mixing gray-scale samples in early while you are building a foundation. If someone only ever appears at night, gray-scale training is acceptable, but keep those samples limited and as clear as possible, and add them only once color recognition is stable for your other people.
-
-</FaqItem>
-
-<FaqItem id="why-cant-i-bulk-upload-photos" question="Why can't I bulk upload photos?">
+### Why can't I bulk upload photos?

 It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.

-</FaqItem>
-
-<FaqItem id="why-cant-i-bulk-reprocess-faces" question="Why can't I bulk reprocess faces?">
+### Why can't I bulk reprocess faces?

 Face embedding models work by breaking apart faces into different features. This means that when reprocessing an image, only images from a similar angle will have its score affected.

-</FaqItem>
-
-<FaqItem id="why-do-unknown-people-score-similarly-to-known-people" question="Why do unknown people score similarly to known people?">
+### Why do unknown people score similarly to known people?

 This can happen for a few different reasons, but this is usually an indicator that the training set needs to be improved. This is often related to over-fitting:

@@ -296,54 +241,33 @@ This can happen for a few different reasons, but this is usually an indicator th

 Review your face collections and remove most of the unclear or low-quality images. Then, use the **Reprocess** button on each face in the **Train** tab to evaluate how the changes affect recognition scores.

-Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower (ideally with different lighting, angles, and conditions) to help the model generalize more effectively.
+Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower - ideally with different lighting, angles, and conditions—to help the model generalize more effectively.

-</FaqItem>
-
-<FaqItem id="should-i-correct-a-face-that-was-recognized-as-the-wrong-person" question="Should I correct a face that was recognized as the wrong person?">
-
-Only if it is a good image. Reassigning a face does add it to that person's training set, but two things are true at once:
-
- Reassigning a single misclassified frame has a small effect. The image is weighted against every other sample for that person, so correcting 1 frame out of 20 will not move recognition much. Occasional wrong guesses on poor frames are normal and do not need to be fixed.
- Reassigning a poor image (blurry, off-angle, low-resolution, gray-scale) can hurt more than the misidentification did, because low-quality samples degrade recognition for that whole person.
-
-So the decision is about image quality, not about the wrong label. If the crop is clear, well-lit, and reasonably front-facing, and it scored low or was wrong, assigning it to the correct person is useful. If you can barely make out the face yourself, ignore it; do not train it just to correct the label.
-
-If a person is repeatedly misidentified, do not keep reassigning the same frame. Instead, remove low-quality or misleading images and add a few high-quality samples to the correct person. See [Why do unknown people score similarly to known people?](#why-do-unknown-people-score-similarly-to-known-people) above.
-
-</FaqItem>
-
-<FaqItem id="frigate-misidentified-a-face-can-i-tell-it-that-a-face-is-not-a-specific-person" question={'Frigate misidentified a face. Can I tell it that a face is "not" a specific person?'}>
+### Frigate misidentified a face. Can I tell it that a face is "not" a specific person?

 No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
 For more guidance, refer to the section above on improving recognition accuracy.

-This also applies to a stranger who is repeatedly matched to a known person (for example, a delivery driver recognized as you). Do not create a profile for them and do not reassign their faces to yourself, as this pollutes your training set and makes recognition worse. Leave the detection as unknown and improve the known person's training set instead. Face recognition learns who someone is, not who they are not.
+### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?

-</FaqItem>
+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.

-<FaqItem id="i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned" question="I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?">
-
-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.
-
-</FaqItem>
-
-### Compatibility and Maintenance
-
-<FaqItem id="can-i-use-other-face-recognition-software-like-doubletake-at-the-same-time-as-the-built-in-face-recognition" question="Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?">
+### Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?

 No, using another face recognition service will interfere with Frigate's built in face recognition. When using double-take the sub_label feature must be disabled if the built in face recognition is also desired.

-</FaqItem>
+### Does face recognition run on the recording stream?

-<FaqItem id="i-get-an-unknown-error-when-taking-a-photo-directly-with-my-iphone" question="I get an unknown error when taking a photo directly with my iPhone">
+Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
+
+1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
+2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
+3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
+
+### I get an unknown error when taking a photo directly with my iPhone

 By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.

-</FaqItem>
-
-<FaqItem id="how-can-i-delete-the-face-database-and-start-over" question="How can I delete the face database and start over?">
+### How can I delete the face database and start over?

 Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.
-
-</FaqItem>
@@ -7,33 +7,33 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";

-Frigate ships with a set of FFmpeg presets to keep your configuration short and readable. Each preset expands to a longer list of FFmpeg arguments at runtime. You can see exactly what every preset expands to in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
+Some presets of FFmpeg args are provided by default to make the configuration easier. All presets can be seen in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).

-In the config file you reference a preset by its name (for example, `preset-vaapi`). In the UI, the same preset is shown with a friendly label (for example, **VAAPI (Intel/AMD GPU)**). Both refer to the same thing: the tables below list the config name alongside the label you'll see in the UI.
+### Hwaccel Presets

-### Hwaccel (Hardware Acceleration) Presets {#hwaccel-presets}
+It is highly recommended to use hwaccel presets in the config. These presets not only replace the longer args, but they also give Frigate hints of what hardware is available and allows Frigate to make other optimizations using the GPU such as when encoding the birdseye restream or when scaling a stream that has a size different than the native stream size.

-Hardware acceleration arguments tell FFmpeg to decode your camera's video stream on a GPU or integrated graphics chip instead of the CPU, which dramatically lowers CPU usage. Using a preset is highly recommended. Beyond replacing a long list of arguments, each preset also tells Frigate what hardware is available so it can offload additional work to the GPU, for example, encoding the Birdseye restream or scaling a stream whose resolution differs from the camera's native size.
+See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more info on how to setup hwaccel for your GPU / iGPU.

-See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md) for details on setting up hardware acceleration for your GPU / iGPU, then select the preset that matches your hardware.
+| Preset                | Usage                          | Other Notes                                           |
+| --------------------- | ------------------------------ | ----------------------------------------------------- |
+| preset-rpi-64-h264    | 64 bit Rpi with h264 stream    |                                                       |
+| preset-rpi-64-h265    | 64 bit Rpi with h265 stream    |                                                       |
+| preset-vaapi          | Intel & AMD VAAPI              | Check hwaccel docs to ensure correct driver is chosen |
+| preset-intel-qsv-h264 | Intel QSV with h264 stream     | If issues occur recommend using vaapi preset instead  |
+| preset-intel-qsv-h265 | Intel QSV with h265 stream     | If issues occur recommend using vaapi preset instead  |
+| preset-nvidia         | Nvidia GPU                     |                                                       |
+| preset-jetson-h264    | Nvidia Jetson with h264 stream |                                                       |
+| preset-jetson-h265    | Nvidia Jetson with h265 stream |                                                       |
+| preset-rkmpp          | Rockchip MPP                   | Use image with \*-rk suffix and privileged mode       |

-| Preset (YAML config)  | UI Label                | Usage                             | Notes                                                           |
-| --------------------- | ----------------------- | --------------------------------- | --------------------------------------------------------------- |
-| preset-rpi-64-h264    | Raspberry Pi (H.264)    | 64-bit Raspberry Pi, H.264 stream |                                                                 |
-| preset-rpi-64-h265    | Raspberry Pi (H.265)    | 64-bit Raspberry Pi, H.265 stream |                                                                 |
-| preset-vaapi          | VAAPI (Intel/AMD GPU)   | Intel or AMD GPU via VAAPI        | Check the hwaccel docs to ensure the correct driver is selected |
-| preset-intel-qsv-h264 | Intel QuickSync (H.264) | Intel QuickSync, H.264 stream     | If you have issues, use the VAAPI preset instead                |
-| preset-intel-qsv-h265 | Intel QuickSync (H.265) | Intel QuickSync, H.265 stream     | If you have issues, use the VAAPI preset instead                |
-| preset-nvidia         | NVIDIA GPU              | NVIDIA GPU                        |                                                                 |
-| preset-jetson-h264    | NVIDIA Jetson (H.264)   | NVIDIA Jetson, H.264 stream       |                                                                 |
-| preset-jetson-h265    | NVIDIA Jetson (H.265)   | NVIDIA Jetson, H.265 stream       |                                                                 |
-| preset-rkmpp          | Rockchip RKMPP          | Rockchip MPP                      | Use an image with the `-rk` suffix and run in privileged mode   |
+Select the appropriate hwaccel preset for your hardware.

 <ConfigTabs>
 <TabItem value="ui">

 1. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to the appropriate preset for your hardware.
-2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and set **Hardware acceleration arguments** for that camera.
+2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and set **Hardware acceleration arguments** for that camera.

 </TabItem>
 <TabItem value="yaml">
@@ -53,25 +53,25 @@ cameras:

 ### Input Args Presets

-Input arguments are passed to FFmpeg before your camera source and control how Frigate connects to and reads the stream: the transport protocol, timeouts, reconnection behavior, and how the stream is probed. The right input args ensure a reliable connection and maximum compatibility for each type of stream.
+Input args presets help make the config more readable and handle use cases for different types of streams to ensure maximum compatibility.

-See [the camera-specific docs](/configuration/camera_specific.md) for more on non-standard cameras and recommendations for using them in Frigate.
+See [the camera specific docs](/configuration/camera_specific.md) for more info on non-standard cameras and recommendations for using them in Frigate.

-| Preset (config)                  | UI Label                                  | Usage                       | Notes                                                                           |
-| -------------------------------- | ----------------------------------------- | --------------------------- | ------------------------------------------------------------------------------- |
-| preset-http-jpeg-generic         | HTTP JPEG (Generic)                       | HTTP live JPEG              | Restreaming the live JPEG is recommended instead                                |
-| preset-http-mjpeg-generic        | HTTP MJPEG (Generic)                      | HTTP MJPEG stream           | Restreaming the MJPEG stream is recommended instead                             |
-| preset-http-reolink              | HTTP - Reolink Cameras                    | Reolink HTTP-FLV stream     | Only for Reolink HTTP, not when restreaming as RTSP                             |
-| preset-rtmp-generic              | RTMP (Generic)                            | RTMP stream                 |                                                                                 |
-| preset-rtsp-generic              | RTSP (Generic)                            | RTSP stream                 | The default when no input args are specified                                    |
-| preset-rtsp-restream             | RTSP - Restream from go2rtc               | RTSP stream from a restream | Use when a go2rtc restream is the source for Frigate                            |
-| preset-rtsp-restream-low-latency | RTSP - Restream from go2rtc (Low Latency) | RTSP stream from a restream | Lowers latency for a go2rtc restream source; may cause issues with some cameras |
-| preset-rtsp-udp                  | RTSP - UDP                                | RTSP stream over UDP        | Use when the camera only supports UDP                                           |
-| preset-rtsp-blue-iris            | RTSP - Blue Iris                          | Blue Iris RTSP stream       | Use when consuming a stream from Blue Iris                                      |
+| Preset                           | Usage                     | Other Notes                                                                                      |
+| -------------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------ |
+| preset-http-jpeg-generic         | HTTP Live Jpeg            | Recommend restreaming live jpeg instead                                                          |
+| preset-http-mjpeg-generic        | HTTP Mjpeg Stream         | Recommend restreaming mjpeg stream instead                                                       |
+| preset-http-reolink              | Reolink HTTP-FLV Stream   | Only for reolink http, not when restreaming as rtsp                                              |
+| preset-rtmp-generic              | RTMP Stream               |                                                                                                  |
+| preset-rtsp-generic              | RTSP Stream               | This is the default when nothing is specified                                                    |
+| preset-rtsp-restream             | RTSP Stream from restream | Use for rtsp restream as source for frigate                                                      |
+| preset-rtsp-restream-low-latency | RTSP Stream from restream | Use for rtsp restream as source for frigate to lower latency, may cause issues with some cameras |
+| preset-rtsp-udp                  | RTSP Stream via UDP       | Use when camera is UDP only                                                                      |
+| preset-rtsp-blue-iris            | Blue Iris RTSP Stream     | Use when consuming a stream from Blue Iris                                                       |

 :::warning

-Be mindful of input arguments when restreaming, because you can end up with a mix of protocols. The `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, using a Reolink camera with an RTSP restream as the recording source while `preset-http-reolink` is applied will cause a crash. In cases like this, set the preset at the stream level instead. See the example below.
+It is important to be mindful of input args when using restream because you can have a mix of protocols. `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, when using a reolink cam with the rtsp restream as a source for record the preset-http-reolink will cause a crash. In this case presets will need to be set at the stream level. See the example below.

 :::

@@ -96,13 +96,13 @@ cameras:

 ### Output Args Presets

-Output arguments are passed to FFmpeg after your camera source and control how recordings are written: which codecs are used and whether audio and video are copied as-is or re-encoded. The right output args ensure consistent, playable recordings for each type of stream.
+Output args presets help make the config more readable and handle use cases for different types of streams to ensure consistent recordings.

-| Preset (config)                  | UI Label                        | Usage                               | Notes                                                                                                                                                             |
-| -------------------------------- | ------------------------------- | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| preset-record-generic            | Record (Generic, no audio)      | Record without audio                | Use this if your camera has no audio, or if you don't want to record audio                                                                                        |
-| preset-record-generic-audio-copy | Record (Generic + Copy Audio)   | Record with the original audio      | Use this to keep the camera's audio in recordings without re-encoding                                                                                             |
-| preset-record-generic-audio-aac  | Record (Generic + Audio to AAC) | Record with audio transcoded to AAC | The default when no output args are specified. Transcodes audio to AAC. If the source is already AAC, use `preset-record-generic-audio-copy` to avoid re-encoding |
-| preset-record-mjpeg              | Record - MJPEG Cameras          | Record an MJPEG stream              | Restreaming the MJPEG stream is recommended instead                                                                                                               |
-| preset-record-jpeg               | Record - JPEG Cameras           | Record a live JPEG                  | Restreaming the live JPEG is recommended instead                                                                                                                  |
-| preset-record-ubiquiti           | Record - Ubiquiti Cameras       | Record a Ubiquiti stream with audio | Handles Ubiquiti's non-standard audio format                                                                                                                      |
+| Preset                           | Usage                             | Other Notes                                                                                                                                                                                              |
+| -------------------------------- | --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| preset-record-generic            | Record WITHOUT audio              | If your camera doesn't have audio, or if you don't want to record audio, use this option                                                                                                                 |
+| preset-record-generic-audio-copy | Record WITH original audio        | Use this to enable audio in recordings                                                                                                                                                                   |
+| preset-record-generic-audio-aac  | Record WITH transcoded aac audio  | This is the default when no option is specified. Use it to transcode audio to AAC. If the source is already in AAC format, use preset-record-generic-audio-copy instead to avoid unnecessary re-encoding |
+| preset-record-mjpeg              | Record an mjpeg stream            | Recommend restreaming mjpeg stream instead                                                                                                                                                               |
+| preset-record-jpeg               | Record live jpeg                  | Recommend restreaming live jpeg instead                                                                                                                                                                  |
+| preset-record-ubiquiti           | Record ubiquiti stream with audio | Recordings with ubiquiti non-standard audio                                                                                                                                                              |
@@ -27,12 +27,13 @@ Running Generative AI models on CPU is not recommended, as high inference times

 You must use a vision-capable model with Frigate. The following models are recommended for local deployment:

-| Model      | Notes                                                                                                                                                                |
-| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.5`  | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
-| `qwen3.6`  | Strong situational understanding, similar to qwen3-vl                                                                                                                |
-| `gemma4`   | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
+| Model         | Notes                                                                                                                                                                |
+| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl`    | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
+| `qwen3.5`     | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
+| `gemma4`      | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
+| `Intern3.5VL` | Relatively fast with good vision comprehension                                                                                                                       |
+| `gemma3`      | Slower model with good vision and temporal understanding                                                                                                             |

 :::info

@@ -48,14 +49,15 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru

 ### Model Types: Instruct vs Thinking

-Vision-language models come in **instruct** variants (fine-tuned to follow instructions and respond concisely), **thinking** variants (fine-tuned for free-form, speculative reasoning), and **hybrid** variants that support both modes per request. Most modern vision-language models are hybrid.
+Most vision-language models are available as **instruct** models, which are fine-tuned to follow instructions and respond concisely to prompts. However, some models (such as certain Qwen-VL or minigpt variants) offer both **instruct** and **thinking** versions.

-Frigate manages reasoning per task automatically:
+- **Instruct models** are always recommended for use with Frigate. These models generate direct, relevant, actionable descriptions that best fit Frigate's object and event summary use case.
+- **Reasoning / Thinking models** are fine-tuned for more free-form, open-ended, and speculative outputs, which are typically not concise and may not provide the practical summaries Frigate expects. For this reason, Frigate does **not** recommend or support using thinking models.

- **Description tasks** (object descriptions, review descriptions, review summaries) are synthesis-only and benefit from concise, direct output, so Frigate disables thinking for these calls when the model exposes a per-request toggle.
- **Chat** lets you toggle thinking on or off from the composer when the configured model supports it.
+Some models are labeled as **hybrid** (capable of both thinking and instruct tasks). In these cases, it is recommended to disable reasoning / thinking, which is generally model specific (see your models documentation).

-You can use a pure instruct, hybrid, or thinking-capable model with Frigate. No extra configuration is required to disable thinking for descriptions.
+**Recommendation:**
+Always select the `-instruct` or documented instruct/tagged variant of any model you use in your Frigate configuration. If in doubt, refer to your model provider's documentation or model library for guidance on the correct model variant to use.

 ### llama.cpp

@@ -293,7 +295,7 @@ Other HTTP options are available, see the [python-genai documentation](https://g

 ### OpenAI

-OpenAI does not have a free tier for their API.
+OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.

 #### Supported Models

@@ -1,70 +0,0 @@
---
-id: go2rtc
-title: go2rtc
---
-
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
-Frigate uses the bundled go2rtc to power a number of key features:
-
- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
- Live stream support for cameras in Home Assistant Integration
- RTSP relay for use with other consumers to reduce the number of connections to your camera streams
-
-:::tip[Most users no longer need to configure go2rtc by hand]
-
-The **camera setup wizard** is the recommended way to add cameras. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />, and the wizard probes your camera and writes its configuration for you, including the go2rtc restream and the live stream mapping, so go2rtc is set up automatically.
-
-This guide is mainly useful if you are **upgrading from an older version and have existing cameras that don't yet use go2rtc**, or if you want to fine-tune a stream by hand (for example, to transcode a codec your browser can't play). The [go2rtc troubleshooting guide](/troubleshooting/go2rtc) applies regardless of how your cameras were added.
-
-:::
-
-## Adding a go2rtc stream manually
-
-If you added your cameras with the wizard, go2rtc is already configured. You can skip straight to [troubleshooting](/troubleshooting/go2rtc). The steps below are for upgrading users with existing cameras that aren't using go2rtc yet, or for anyone who prefers to configure a stream by hand.
-
-Configure go2rtc to connect to your camera by adding the stream you want to use for live view. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-streams), not just rtsp.
-
-:::tip
-
-For the best experience, set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
-
-See [the live view docs](/configuration/live#setting-streams-for-live-ui) for more information.
-
-:::
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and click **Add stream**. Give the stream a name (use the camera's name so Frigate can auto-map it - for example, if your camera's name is `back`, use `back` as the go2rtc stream name), then paste the camera's stream URL into the **Source** field. Save the section.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-go2rtc:
-  streams:
-    back:
-      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-```
-
-</TabItem>
-</ConfigTabs>
-
-After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
-
-### Next steps
-
-1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
-2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
-3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
-
-## Troubleshooting
-
-If your stream won't play, has no audio, uses excessive CPU, or otherwise misbehaves, see the dedicated [go2rtc troubleshooting guide](/troubleshooting/go2rtc). It walks through how to isolate where the problem is and covers the most common issues: unsupported codecs, H.265/HEVC, audio, WebRTC and two-way talk, hardware-accelerated transcoding with FFmpeg 8, and camera-specific quirks.
-
-## Homekit Configuration
-
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
@@ -17,6 +17,8 @@ Some types of hardware acceleration are detected and used automatically, but you
 - Check the logs: A message will either say that hardware acceleration was automatically detected, or there will be a warning that no hardware acceleration was automatically detected
 - If hardware acceleration is specified in the config, verification can be done by ensuring the logs are free from errors. There is no CPU fallback for hardware acceleration.

+:::info
+
 Frigate supports presets for optimal hardware accelerated video decoding:

 **AMD**
@@ -47,10 +49,14 @@ Frigate supports presets for optimal hardware accelerated video decoding:

 Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro

+:::
+
 ## Intel-based CPUs

 Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video decoding.

+:::info
+
 **Recommended hwaccel Preset**

 | CPU Generation     | Intel Driver | Recommended Preset  | Notes                                       |
@@ -62,9 +68,11 @@ Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video
 | Intel Arc A-series | iHD / Xe     | preset-intel-qsv-\* |                                             |
 | Intel Arc B-series | iHD / Xe     | preset-intel-qsv-\* | Requires host kernel 6.12+                  |

+:::
+
 :::note

-The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
+The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).

 See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html) to figure out what generation your CPU is.

@@ -77,7 +85,7 @@ VAAPI supports automatic profile selection so it will work automatically with bo
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -97,7 +105,7 @@ ffmpeg:
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -115,7 +123,7 @@ ffmpeg:
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -161,7 +169,7 @@ Frigate can utilize modern AMD integrated GPUs and AMD GPUs to accelerate video

 ### Configuring Radeon Driver

-You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
+You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).

 ### Via VAAPI

@@ -170,7 +178,7 @@ VAAPI supports automatic profile selection so it will work automatically with bo
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -185,7 +193,7 @@ ffmpeg:

 ## NVIDIA GPUs

-While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced/system#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
+While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.

 A more complete list of cards and their compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).

@@ -229,7 +237,7 @@ Using `preset-nvidia` ffmpeg will automatically select the necessary profile for
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -292,7 +300,7 @@ If you are using the HA App, you may need to use the full access variant and tur
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -412,7 +420,7 @@ For example, for H264 video, you'll select `preset-jetson-h264`.
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -444,7 +452,7 @@ Set the FFmpeg hwaccel preset to enable hardware video processing.
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -477,7 +485,7 @@ Error marking filters as finished
 Restarting ffmpeg...
 ```

-you should try to upgrade to FFmpeg 7. This can be done using this config option:
+you should try to uprade to FFmpeg 7. This can be done using this config option:

 ```yaml
 ffmpeg:
@@ -511,7 +519,7 @@ Set the FFmpeg hwaccel args to enable hardware video processing.
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />.
+Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.

 </TabItem>
 <TabItem value="yaml">
@@ -1,5 +1,5 @@
 ---
-id: config
+id: index
 title: Frigate Configuration
 ---

@@ -7,58 +7,15 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";

-Frigate can be configured through the **Settings UI** or by editing the YAML configuration file directly. The Settings UI is the recommended approach. It provides validation and a guided experience for all configuration options.
+Frigate can be configured through the **Settings UI** or by editing the YAML configuration file directly. The Settings UI is the recommended approach — it provides validation and a guided experience for all configuration options.

-## Using the Settings UI
-
-The Settings UI groups every configuration option into sections that are listed in the left-hand menu. Each section presents a guided form with validation, so you don't need to remember the structure of the YAML or look up option names by hand.
-
-### Global vs. camera-level configuration
-
-Settings are organized into two scopes:
-
- **Global configuration**: values under <NavPath path="Settings > Global configuration" /> apply to every camera by default. This is where you set the baseline behavior for object detection, recording, snapshots, motion, and so on.
- **Camera configuration**: values under <NavPath path="Settings > Camera configuration" /> apply to a single camera. Use the camera selector button at the top of these pages to choose which camera you are editing.
-
-When a camera-level section is left untouched, the camera simply inherits the global values. Changing a value on a camera page **overrides** the global value for that camera only: the global setting and every other camera are unaffected. This mirrors how the YAML works, where a value set under `cameras.<name>` takes precedence over the same value set at the top level.
-
-To undo an override and go back to inheriting from the parent scope, use the reset button at the bottom of the section:
-
- On a camera section, the button is labeled **Reset to Global** and restores the camera to the global value.
- On a global section, the button is labeled **Reset to Default** and restores Frigate's built-in default.
-
-Resetting asks for confirmation and cannot be undone once applied.
-
-### Saving changes and the Save All button
-
-Edits are not applied until you save them. As soon as you change a value, the UI tracks it as a pending change:
-
- The edited section shows a **Modified** badge, and the changed fields are highlighted.
- A **You have unsaved changes** notice appears above the section's **Save** and **Undo** buttons. **Save** commits just that section; **Undo** discards its pending edits.
-
-Because pending changes can span multiple sections (and multiple cameras), the header provides a **Save All** button that writes every pending change at once. Next to it, **Review pending changes** opens a summary that lists each pending edit with its scope (Global or a specific camera), the affected field, and the new value, so you can confirm exactly what will be written before committing. **Undo All** discards every pending change across all sections.
-
-### Restart-required indicators
-
-Most settings take effect immediately, but some require Frigate to restart before they apply. Fields that require a restart are marked with a small restart icon and a **Restart required** tooltip next to the field label.
-
-When you save a change that touches one of these fields, Frigate confirms the save and reminds you that a restart is needed (for example, _"Settings saved successfully. Restart Frigate to apply your changes."_). The notification includes a one-click **Restart Frigate** action so you can apply the change right away, or you can continue editing and restart later.
-
-### The colored dots in the camera configuration menu
-
-When you are working under <NavPath path="Settings > Camera configuration" />, small colored dots can appear next to a section's name in the menu. They give you an at-a-glance summary of that section's state for the selected camera:
-
- **Blue dot**: this section **overrides the global configuration**. One or more values in the section have been set specifically for this camera and differ from the global defaults.
- **Profile-colored dot**: when you are viewing a [camera profile](./profiles.md), a dot in that profile's assigned color indicates the section is **overridden by that profile**. Each profile is given its own distinct color so you can tell at a glance which sections it changes.
- **Amber dot**: this section has **unsaved changes**. It appears alongside the **Modified** badge whenever you have pending edits in the section that haven't been saved yet.
-
-Hover over any dot to see a tooltip describing what it means. Open a section to see exactly which fields are overridden: the section header indicates how many fields differ from the global (or base) configuration.
+It is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).

 ## Configuration File Location

-For users who prefer to edit the YAML configuration file directly, it is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
+For users who prefer to edit the YAML configuration file directly:

- **Home Assistant App:** `/addon_configs/<addon_directory>/config.yml` (see [directory list](#accessing-app-config-dir))
+- **Home Assistant App:** `/addon_configs/<addon_directory>/config.yml` — see [directory list](#accessing-app-config-dir)
 - **All other installations:** Map to `/config/config.yml` inside the container

 It can be named `config.yml` or `config.yaml`, but if both files exist `config.yml` will be preferred and `config.yaml` will be ignored.
@@ -100,7 +57,7 @@ VS Code supports JSON schemas for automatically validating configuration files.

 ## Environment Variable Substitution

-Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./advanced/reference.md). For example, the following values can be replaced at runtime by using environment variables:
+Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./reference.md). For example, the following values can be replaced at runtime by using environment variables:

 ```yaml
 mqtt:
@@ -135,7 +92,7 @@ genai:

 ## Common configuration examples

-Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./advanced/reference.md) for detailed information about all config values.
+Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./reference.md) for detailed information about all config values.

 ### Raspberry Pi Home Assistant App with USB Coral

@@ -156,7 +113,7 @@ Here are some common starter configuration examples. These can be configured thr
 3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
 4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
+6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
 7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp

 </TabItem>
@@ -235,7 +192,7 @@ cameras:
 3. Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
 4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 5. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-6. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
+6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
 7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp

 </TabItem>
@@ -313,7 +270,7 @@ cameras:
 4. On the same page, in the **Custom Model** tab, configure the OpenVINO model path and settings
 5. Navigate to <NavPath path="Settings > Global configuration > Recording" /> and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
 6. Navigate to <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-7. Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and add your camera with the appropriate RTSP stream URL
+7. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
 8. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp

 </TabItem>
@@ -6,7 +6,6 @@ title: License Plate Recognition (LPR)
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
-import FaqItem from "@site/src/components/FaqItem";

 Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) 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.

@@ -284,8 +283,8 @@ Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /
 | Field                          | Description                                                                                           |
 | ------------------------------ | ----------------------------------------------------------------------------------------------------- |
 | **Enable LPR**                 | Set to on                                                                                             |
-| **Minimum plate area**         | Set to `1500` to ignore plates with an area (length x width) smaller than 1500 pixels                  |
-| **Min plate length**           | Set to `4` to only recognize plates with 4 or more characters                                          |
+| **Minimum plate area**         | Set to `1500` — ignore plates with an area (length x width) smaller than 1500 pixels                  |
+| **Min plate length**           | Set to `4` — only recognize plates with 4 or more characters                                          |
 | **Known plates > Wife's Car**  | `ABC-1234`, `ABC-I234` (accounts for potential confusion between the number one and capital letter I) |
 | **Known plates > Johnny**      | `J*N-*234` (matches JHN-1234 and JMN-I234; `*` matches any number of characters)                      |
 | **Known plates > Sally**       | `[S5]LL 1234` (matches both SLL 1234 and 5LL 1234)                                                    |
@@ -364,7 +363,7 @@ An example configuration for a dedicated LPR camera using a `license_plate`-dete

 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).

-Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
+Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.

 Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.

@@ -474,15 +473,15 @@ Navigate to <NavPath path="Settings > Camera configuration > License plate recog
 | Field                 | Description                                                                      |
 | --------------------- | -------------------------------------------------------------------------------- |
 | **Enable LPR**        | Set to on                                                                        |
-| **Enhancement level** | Set to `3` (optional, enhances the image before trying to recognize characters) |
+| **Enhancement level** | Set to `3` (optional — enhances the image before trying to recognize characters) |

-Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
+Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.

 Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.

 | Field                       | Description                                                                                                                  |
 | --------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
-| **Enable object detection** | Set to off to disable Frigate's standard object detection pipeline                                                           |
+| **Enable object detection** | Set to off — disables Frigate's standard object detection pipeline                                                           |
 | **Detect FPS**              | Set to `5`. Increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU. |
 | **Detect width**            | Set to `1920` (recommended value, but depends on your camera)                                                                |
 | **Detect height**           | Set to `1080` (recommended value, but depends on your camera)                                                                |
@@ -491,7 +490,7 @@ Navigate to <NavPath path="Settings > Camera configuration > Objects" />.

 | Field                | Description                                                                            |
 | -------------------- | -------------------------------------------------------------------------------------- |
-| **Objects to track** | Set to an empty list, required when not using a Frigate+ model for dedicated LPR mode |
+| **Objects to track** | Set to an empty list — required when not using a Frigate+ model for dedicated LPR mode |

 Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.

@@ -592,9 +591,7 @@ By selecting the appropriate configuration, users can optimize their dedicated L

 ## FAQ

-### Detection and Recognition
-
-<FaqItem id="why-isnt-my-license-plate-being-detected-and-recognized" question="Why isn't my license plate being detected and recognized?">
+### Why isn't my license plate being detected and recognized?

 Ensure that:

@@ -609,43 +606,29 @@ 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.

-</FaqItem>
-
-<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
+### Can I run LPR without detecting `car` or `motorcycle` objects?

 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.

-</FaqItem>
-
-<FaqItem id="how-can-i-improve-detection-accuracy" question="How can I improve detection accuracy?">
+### How can I improve detection accuracy?

 - Use high-quality cameras with good resolution.
 - Adjust `detection_threshold` and `recognition_threshold` values.
 - Define a `format` regex to filter out invalid detections.

-</FaqItem>
-
-<FaqItem id="does-lpr-work-at-night" question="Does LPR work at night?">
+### Does LPR work at night?

 Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.

-</FaqItem>
-
-<FaqItem id="can-i-limit-lpr-to-specific-zones" question="Can I limit LPR to specific zones?">
+### Can I limit LPR to specific zones?

 LPR, like other Frigate enrichments, runs at the camera level rather than the zone level. While you can't restrict LPR to specific zones directly, you can control when recognition runs by setting a `min_area` value to filter out smaller detections.

-</FaqItem>
-
-<FaqItem id="how-can-i-match-known-plates-with-minor-variations" question="How can I match known plates with minor variations?">
+### How can I match known plates with minor variations?

 Use `match_distance` to allow small character mismatches. Alternatively, define multiple variations in `known_plates`.

-</FaqItem>
-
-### Performance and Troubleshooting
-
-<FaqItem id="how-do-i-debug-lpr-issues" question="How do I debug LPR issues?">
+### How do I debug LPR issues?

 Start with ["Why isn't my license plate being detected and recognized?"](#why-isnt-my-license-plate-being-detected-and-recognized). If you are still having issues, work through these steps.

@@ -688,7 +671,7 @@ lpr:
 3. Ensure your plates are being _detected_.

   If you are using a Frigate+ or `license_plate` detecting model:
-   - Watch the [Debug view](/usage/live#the-single-camera-view) to ensure that `license_plate` is being detected.
+   - Watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected.
   - View MQTT messages for `frigate/events` to verify detected plates.
   - You may need to adjust your `min_score` and/or `threshold` for the `license_plate` object if your plates are not being detected.

@@ -697,28 +680,21 @@ lpr:
   - You may need to adjust your `detection_threshold` if your plates are not being detected.

 4. Ensure the characters on detected plates are being _recognized_.
-   - Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
   - 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.
   - 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 `recognition_threshold` settings per the suggestions [above](#advanced-configuration).

-</FaqItem>
-
-<FaqItem id="will-lpr-slow-down-my-system" question="Will LPR slow down my system?">
+### Will LPR slow down my system?

 LPR's performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU or GPU for optimal results. If you are running the Dedicated LPR Camera mode, resource usage will be higher compared to users who run a model that natively detects license plates. Tune your motion detection settings for your dedicated LPR camera so that the license plate detection model runs only when necessary.

-</FaqItem>
-
-<FaqItem id="i-am-seeing-a-yolov9-plate-detection-metric-in-enrichment-metrics-but-i-have-a-frigate-or-custom-model-that-detects-license_plate-why-is-the-yolov9-model-running" question={<>I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects <code>license_plate</code>. Why is the YOLOv9 model running?</>}>
+### I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects `license_plate`. Why is the YOLOv9 model running?

 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` 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.

-</FaqItem>
-
-<FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
+### It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?

 This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.

@@ -726,10 +702,6 @@ 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.

-</FaqItem>
-
-<FaqItem id="i-see-error-running--model-in-my-logs-or-my-inference-time-is-very-high-how-can-i-fix-this" question={'I see "Error running ... model" in my logs, or my inference time is very high. 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.
-
-</FaqItem>
@@ -11,7 +11,7 @@ Frigate intelligently displays your camera streams on the Live view dashboard. B

 ### Live View technologies

-Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be [configured](/configuration/go2rtc).
+Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).

 The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.

@@ -88,18 +88,8 @@ Configure a "friendly name" for your stream followed by the go2rtc stream name.
 <ConfigTabs>
 <TabItem value="ui">

-1. Navigate to <NavPath path="Settings > Camera configuration > Live playback" /> and select your camera.
-2. Under **Live stream names**, click **Add stream** to add a new entry.
-3. In the **Stream name** field, enter a friendly name that will appear in the Live UI's stream dropdown (e.g., `Main Stream`).
-4. In the **go2rtc stream** field, open the dropdown and select the go2rtc stream this name should map to (e.g., `test_cam`). The dropdown lists every stream configured under `go2rtc.streams`. If the go2rtc stream hasn't been created yet, you can type the name and choose **Use "..."** to save a custom value.
-5. Repeat for each additional stream you want to expose (e.g., `Sub Stream` → `test_cam_sub`).
-6. Use the trash icon on a row to remove a stream, then **Save** the section.
-
-:::tip
-
-Configure your go2rtc streams first under <NavPath path="Settings > System > go2rtc streams" /> so the dropdown is populated with valid options.
-
-:::
+1. Navigate to <NavPath path="Settings > Camera configuration > Live playback" />, then select your camera.
+   - Under **Live stream names**, add entries mapping a friendly name to each go2rtc stream name (e.g., `Main Stream` mapped to `test_cam`, `Sub Stream` mapped to `test_cam_sub`).

 </TabItem>
 <TabItem value="yaml">
@@ -267,47 +257,19 @@ cameras:
 </TabItem>
 </ConfigTabs>

-### Camera state
+### Disabling cameras

-Each camera has three possible states, surfaced as a status selector in **Settings → Global configuration → Camera management**:
+Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI.

- **On**: streams are processed normally. Object detection, recording, and Live view are active.
- **Off**: Frigate's ffmpeg processes are paused. Recording stops, object detection is paused, and the Live dashboard displays a blank image with a "Camera is off" message. The camera is still visible in the Live dashboard and its past review items, tracked objects, and historical footage remain accessible via the UI. The Off state persists across Frigate restarts via a `.runtime_state.json` file alongside `config.yml` (see [Runtime toggle persistence](#runtime-toggle-persistence)).
- **Disabled**: the change is saved to your configuration file (`enabled: False`). The camera stops immediately, Frigate stops ffmpeg processes, and all live and historical UI elements for the camera are no longer visible but remains retained on disk. The camera is still listed in **Settings → Global configuration → Camera management** so it can be re-enabled. **A restart of Frigate is required to bring a disabled camera back to On.**
+:::note

-#### Turning a camera on or off
+Disabling a camera via the Frigate UI or MQTT is temporary and does not persist through restarts of Frigate.

-Turning a camera off is temporary and does not require a restart. The available controls are:
+:::

- The power button in the single-camera Live view header
- The right-click context menu on a camera tile on the Live dashboard
- The Camera management settings pane (status set to **Off**)
- The mobile settings drawer on the single-camera Live view (admin users only)
- The [MQTT topic](/integrations/mqtt#frigatecamera_nameenabledset) `frigate/<camera_name>/enabled/set` with payload `ON` or `OFF`
- The Home Assistant integration via the [`camera.turn_on` / `camera.turn_off` actions](/integrations/home-assistant#camera-api)
+For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).

-#### Disabling a camera
-
-Disabling a camera saves the change to your configuration file. Navigate to **Settings → Global configuration → Camera management** and set the camera's status to **Disabled**. Runtime processing stops immediately; the change persists across restarts.
-
-Re-enabling a disabled camera requires a restart of Frigate so that the ffmpeg processes and other camera-scoped resources can be initialized. The UI will prompt you to restart when you switch a disabled camera back to On.
-
-#### Restream behavior
-
-For both Off and Disabled cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
-
-#### Choosing Off versus Disabled
-
-If you want a camera's historical data (review items, tracked objects, footage) to stay accessible in the UI while you stop processing, set the camera to **Off**. If you want the camera fully removed from the Live dashboard, review filters, and other UI surfaces, set it to **Disabled**. The Disabled state still keeps the camera in Camera management so it can be re-enabled later; if you want to remove all traces of a camera including its configuration, delete it via Camera management instead.
-
-#### Runtime toggle persistence
-
-The Live view toggles for **camera on/off**, **detect**, **recordings**, **snapshots**, and **audio detection** (along with the equivalent MQTT `/set` topics) write the new state to `.runtime_state.json` next to your `config.yml`. The file is replayed on Frigate startup so your last-known toggle states survive a restart. Two interactions worth knowing:
-
- **Settings UI saves win.** When you save a field through **Settings → Global configuration**, the matching entry is cleared from `.runtime_state.json` so the new value in your config file is the durable source.
- **Switching profiles clears all runtime overrides.** Activating or deactivating a [profile](/configuration/profiles) is treated as a deliberate state change, so the file is wiped to avoid stale overrides replaying on top of the new profile.
-
-If you hand-edit `config.yml` while runtime overrides exist, the overrides will still replay on restart. Delete `.runtime_state.json` to reset to the YAML-defined defaults.
+Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.

 ### Live player error messages

@@ -333,7 +295,7 @@ When your browser runs into problems playing back your camera streams, it will l

 - **stalled**
  - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.

  - Possible console messages from the player code:
    - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
@@ -371,7 +333,7 @@ When your browser runs into problems playing back your camera streams, it will l
   - Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
   - Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
   - Test with a different stream via the UI dropdown (if `live -> streams` is configured).
-   - For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
+   - For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see (WebRTC Extra Configuration)(#webrtc-extra-configuration)).
   - If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.

 3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
@@ -432,9 +394,3 @@ When your browser runs into problems playing back your camera streams, it will l
             roles:
               - detect
   ```
-
-   The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
-
-9. **Why does Frigate prefer MSE over WebRTC for live view?**
-
-   Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
@@ -7,11 +7,9 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";

-Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
-
 ## Motion masks

-Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the [Debug view](/usage/live#the-single-camera-view) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
+Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the Debug feed (Settings --> Debug) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._

 See [further clarification](#further-clarification) below on why you may not want to use a motion mask.

@@ -23,16 +21,7 @@ Object filter masks can be used to filter out stubborn false positives in fixed

 ![object mask](/img/bottom-center-mask.jpg)

-## Which tool do I need?
-
-| What you're trying to do                                                                                                                                                                  | Recommended tool                                                                                            | How it works                                                                                                                                                                                |
-| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Only get alerts/detections for activity in the areas you care about, ignoring activity elsewhere (e.g., alert when someone enters your yard, but not when they walk past on the sidewalk) | A [zone](zones.md) combined with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) | Frigate keeps detecting and tracking activity everywhere in the frame, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
-| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person)                                                                         | An **object filter mask** for that object type                                                              | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded.                                                      |
-| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying)                                                            | A **motion mask**                                                                                           | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
-| Stop tracking an object type altogether on this camera (e.g., you never care about cats)                                                                                                  | Remove the object from the camera's [`objects.track`](objects.md) list                                      | Frigate skips this object type entirely on this camera, regardless of where it appears.                                                                                                     |
-
-## Using the mask creator
+## Creating masks

 <ConfigTabs>
 <TabItem value="ui">
@@ -135,14 +124,3 @@ This is what `required_zones` are for. You should define a zone (remember this i
 > Maybe my specific situation just warrants this. I've just been having a hard time understanding the relevance of this information - it seems to be that it's exactly what would be expected when "masking out" an area of ANY image.

 That may be the case for you. Frigate will definitely work harder tracking people on the sidewalk to make sure it doesn't miss anyone who steps foot on your stoop. The trade off with the way you have it now is slower recognition of objects and potential misses. That may be acceptable based on your needs. Also, if your resolution is low enough on the detect stream, your regions may already be so big that they grab the entire object anyway.
-
-## Common mistakes
-
-**"I added a motion mask to ignore my driveway/sidewalk."**
-A motion mask doesn't hide an area from Frigate. Objects can still be detected and tracked inside a masked area. The mask only stops motion _in that area_ from triggering object detection. If you want activity on the sidewalk to never produce a review item, define a [zone](zones.md) over the area you DO care about (your stoop, your driveway) and add it to [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). Frigate will still see people on the sidewalk, but it won't create an alert until they cross into the zone.
-
-**"I added an object filter mask because I don't care about cars in my yard."**
-Object filter masks are for stubborn false positives at fixed locations, not for filtering whole areas or whole object types. If you only want alerts when a car enters the driveway, use a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). If you don't care about a whole object type on this camera, remove it from [`objects.track`](objects.md).
-
-**"I masked everything except a thin strip on my stoop."**
-Heavy masking hurts tracking. Frigate uses motion near a tracked object's previous bounding box to decide where to look in the next frame; with most of the frame masked, an object walking from an unmasked area into a masked one effectively disappears and gets picked up as a "new" object when it reappears. For example: someone walks down your sidewalk, stops under a tree (masked area) to tie their shoe, then continues. Frigate sees that as two separate people and can create two separate review items. Because Frigate needs several consecutive frames above the confidence threshold to commit to a detection, each re-appearance can also delay or miss alerts. Use [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) for "only alert me about this spot" and leave the surrounding area unmasked so tracking stays intact.
@@ -59,8 +59,6 @@ Metrics are available at `/api/metrics` by default. No additional Frigate config
 - `frigate_storage_used_bytes{storage=""}` - Storage used bytes
 - `frigate_storage_mount_type{mount_type="", storage=""}` - Storage mount type info

-These gauges report the operating system's figures for the whole filesystem (the same numbers as `df`), not Frigate's own recording footprint. For how this differs from the recordings usage shown in the UI, see [Understanding storage usage](/configuration/record#understanding-storage-usage).
-
 ### Service Metrics

 - `frigate_service_uptime_seconds` - Uptime in seconds
@@ -11,7 +11,7 @@ import NavPath from "@site/src/components/NavPath";

 Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.

-Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the [debug viewer](/usage/live#the-single-camera-view).
+Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the debug viewer.

 ## The Goal

@@ -66,7 +66,7 @@ motion:
 </TabItem>
 </ConfigTabs>

-Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dog blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.
+Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dogs blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.

 Watching the motion boxes in the debug view, increase the threshold until you only see motion that is visible to the eye. Once this is done, it is important to test and ensure that desired motion is still detected.

@@ -151,7 +151,7 @@ motion:

 Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. `lightning_threshold` defines the percentage of the image used to detect these substantial changes. Increasing this value makes motion detection more likely to treat large changes (like IR mode switches) as valid motion. Decreasing it makes motion detection more likely to ignore large amounts of motion, such as a person approaching a doorbell camera.

-Note that `lightning_threshold` does **not** stop motion-based recordings from being saved. It only prevents additional motion analysis after the threshold is exceeded, reducing false positive object detections during high-motion periods (e.g. storms or PTZ sweeps) without interfering with recordings.
+Note that `lightning_threshold` does **not** stop motion-based recordings from being saved — it only prevents additional motion analysis after the threshold is exceeded, reducing false positive object detections during high-motion periods (e.g. storms or PTZ sweeps) without interfering with recordings.

 :::warning

@@ -194,10 +194,6 @@ This option is handy when you want to prevent large transient changes from trigg

 :::warning

-When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise. They typically only take up a few megabytes and are quick to scan in the timeline UI.
+When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise — they typically only take up a few megabytes and are quick to scan in the timeline UI.

 :::
-
-## Reviewing Detected Motion
-
-To review what the detector picked up, or to search past recordings for motion in a specific region, see [Reviewing Motion](/usage/review#reviewing-motion) on the Review page.
@@ -7,7 +7,7 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";

-There are several types of object filters that can be used to reduce [false positive](/frigate/glossary#false-positive) rates.
+There are several types of object filters that can be used to reduce false positive rates.

 ## Object Scores

@@ -26,9 +26,9 @@ In frame 2, the score is below the `min_score` value, so Frigate ignores it and

 The **top score** is the highest computed score the tracked object has ever reached during its lifetime. Because the computed score rises and falls as new frames come in, the top score can be thought of as the peak confidence Frigate had in the object. In Frigate's UI (such as the Tracking Details pane in Explore), you may see all three values:

- **Score**: the raw detector score for that single frame.
- **Computed Score**: the median of the most recent score history at that moment. This is the value compared against `threshold`.
- **Top Score**: the highest computed score reached so far for the tracked object.
+- **Score** — the raw detector score for that single frame.
+- **Computed Score** — the median of the most recent score history at that moment. This is the value compared against `threshold`.
+- **Top Score** — the highest computed score reached so far for the tracked object.

 ### Minimum Score

@@ -36,7 +36,7 @@ Any detection below `min_score` will be immediately thrown out and never tracked

 ### Threshold

-`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create a tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
+`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.

 ## Configuring Object Scores

@@ -144,8 +144,8 @@ cameras:

 ### Zones

-[Required zones](/configuration/zones.md#restricting-alerts-and-detections-to-specific-zones) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
+[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.

 ### Object Masks

-[Object Filter Masks](/configuration/masks#object-filter-masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape. Object filter masks can be configured in <NavPath path="Settings > Camera configuration > Masks / Zones" />.
+[Object Filter Masks](/configuration/masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape. Object filter masks can be configured in <NavPath path="Settings > Camera configuration > Masks / Zones" />.
@@ -158,4 +158,4 @@ Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use yo
 - EdgeTPU Model: `/edgetpu_model.tflite`
 - Labels: `/labelmap.txt`

-You also need to update the [model config](advanced/system.md#model) if they differ from the defaults.
+You also need to update the [model config](advanced.md#model) if they differ from the defaults.
@@ -14,13 +14,13 @@ Profiles allow you to define named sets of camera configuration overrides that c
 Profiles operate as a two-level system:

 1. **Profile definitions** are declared at the top level of your config under `profiles`. Each definition has a machine name (the key) and a `friendly_name` for display in the UI.
-2. **Camera profile overrides** are declared under each camera's `profiles` section, keyed by the profile name. Only the settings you want to change need to be specified. Everything else is inherited from the camera's base configuration.
+2. **Camera profile overrides** are declared under each camera's `profiles` section, keyed by the profile name. Only the settings you want to change need to be specified — everything else is inherited from the camera's base configuration.

 When a profile is activated, Frigate merges each camera's profile overrides on top of its base config. When the profile is deactivated, all cameras revert to their original settings. Only one profile can be active at a time.

 :::info

-Profile changes are applied in-memory and take effect immediately. No restart is required. The active profile is persisted across Frigate restarts (stored in the `/config/.profiles` file).
+Profile changes are applied in-memory and take effect immediately — no restart is required. The active profile is persisted across Frigate restarts (stored in the `/config/.profiles` file).

 :::

@@ -33,10 +33,10 @@ The easiest way to define profiles is to use the Frigate UI. Profiles can also b
 <ConfigTabs>
 <TabItem value="ui">

-1. **Create a profile**: Navigate to <NavPath path="Settings > Global configuration > Profiles" />. Click the **Add Profile** button, enter a name (and optionally a profile ID).
-2. **Configure overrides**: Navigate to a camera configuration section (e.g. Motion detection, Record, Notifications). In the top right, two buttons will appear - choose a camera and a profile from the profile selector to edit overrides for that camera and section. Only the fields you change will be stored as overrides. Fields that require a restart are hidden since profiles are applied at runtime. You can click the **Remove Profile Override** button to clear overrides.
-3. **Activate a profile**: Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to <NavPath path="Settings > Global configuration > Profiles" />, then choose a profile in the Active Profile dropdown to activate it. The active profile is also shown in the status bar at the bottom of the screen on desktop browsers.
-4. **Delete a profile**: Navigate to <NavPath path="Settings > Global configuration > Profiles" />, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
+1. **Create a profile** — Navigate to <NavPath path="Settings > Camera configuration > Profiles" />. Click the **Add Profile** button, enter a name (and optionally a profile ID).
+2. **Configure overrides** — Navigate to a camera configuration section (e.g. Motion detection, Record, Notifications). In the top right, two buttons will appear - choose a camera and a profile from the profile selector to edit overrides for that camera and section. Only the fields you change will be stored as overrides — fields that require a restart are hidden since profiles are applied at runtime. You can click the **Remove Profile Override** button to clear overrides.
+3. **Activate a profile** — Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to <NavPath path="Settings > Camera configuration > Profiles" />, then choose a profile in the Active Profile dropdown to activate it. The active profile is also shown in the status bar at the bottom of the screen on desktop browsers.
+4. **Delete a profile** — Navigate to <NavPath path="Settings > Camera configuration > Profiles" />, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.

 </TabItem>
 <TabItem value="yaml">
@@ -126,11 +126,7 @@ Only the fields you explicitly set in a profile override are applied. All other

 ## Activating Profiles

-Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), or the Home Assistant integration.
-
-In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
-
-Activating or deactivating a profile clears any [runtime toggle overrides](/configuration/live#runtime-toggle-persistence) so the profile's settings aren't silently undone by a stale toggle from before the switch.
+Profiles can be activated and deactivated from the Frigate UI. Open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.

 ## Example: Home / Away Setup

@@ -139,10 +135,10 @@ A common use case is having different detection and notification settings based
 <ConfigTabs>
 <TabItem value="ui">

-1. Navigate to <NavPath path="Settings > Global configuration > Profiles" /> and create two profiles: **Home** and **Away**.
+1. Navigate to <NavPath path="Settings > Camera configuration > Profiles" /> and create two profiles: **Home** and **Away**.
 2. From to the Camera configuration section in Settings, choose the **front_door** camera, and select the **Away** profile from the profile dropdown. Then, enable notifications from the Notifications pane, and set alert labels to `person` and `car` from the Review pane. Then, from the profile dropdown choose **Home** profile, then navigate to Notifications to disable notifications.
 3. For the **indoor_cam** camera, perform similar steps - configure the **Away** profile to enable the camera, detection, and recording. Configure the **Home** profile to disable the camera entirely for privacy.
-4. Activate the desired profile from <NavPath path="Settings > Global configuration > Profiles" /> or from the **Profiles** option in Frigate's main menu.
+4. Activate the desired profile from <NavPath path="Settings > Camera configuration > Profiles" /> or from the **Profiles** option in Frigate's main menu.

 </TabItem>
 <TabItem value="yaml">
@@ -211,33 +207,3 @@ In this example:
 - **Away profile**: The front door camera enables notifications and tracks specific alert labels. The indoor camera is fully enabled with detection and recording.
 - **Home profile**: The front door camera disables notifications. The indoor camera is completely disabled for privacy.
 - **No profile active**: All cameras use their base configuration values.
-
-## FAQ
-
-### Can I define a zone or mask in a profile but not have it in the base config?
-
-No. Profiles are pure overrides. Every zone and mask defined under a profile must reference an entry that already exists on the base camera config. Configurations that introduce profile-only zones or masks are rejected at startup.
-
-If you want a zone or mask to be active only under a specific profile, define it on the base config with `enabled: false`, then enable it in that profile's overrides.
-
-### How do I revert a profile zone or mask override back to the base configuration?
-
-Delete the override. In the Frigate UI, edit the profile and use the "Revert override" action (the trash can icon) on the zone or mask. The base entry is left untouched, and once the override is removed the profile inherits the base values for that zone or mask.
-
-### Can multiple profiles be active at the same time?
-
-No. Only one profile can be active at a time. Activating a new profile automatically deactivates the current one.
-
-### What happens to my profile overrides if I delete a zone or mask from the base?
-
-When you delete a base zone or mask in the Frigate UI, any profile overrides for that entry are deleted automatically as part of the same operation. If you remove a base entry by editing your config file directly and leave a profile override behind, the config will fail validation at startup until the orphaned override is removed as well.
-
-### Why are some settings missing when I configure a profile override?
-
-Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
-
-### Can I schedule profiles to be enabled or disabled at certain times?
-
-Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.
-
-If you prefer something lightweight, a simple script driven by a cron job that toggles profiles on a schedule works too.
@@ -11,12 +11,6 @@ Recordings can be enabled and are stored at `/media/frigate/recordings`. The fol

 New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.

-:::tip
-
-To keep a specific clip beyond your retention window, [export](/usage/exports) it rather than increasing retention for the whole camera. Exports are saved separately and are never removed by retention.
-
-:::
-
 H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other browsers require recordings to be encoded with H264.

 ## Common recording configurations
@@ -170,9 +164,9 @@ record:

 The `pre_capture` and `post_capture` values define the **time window** around a review item, but only recording segments that also match the configured **retention mode** are actually kept on disk.

- **`mode: all`**: Retains every segment within the capture window, regardless of whether motion was detected.
- **`mode: motion`** (default): Only retains segments within the capture window that contain motion. This includes segments with active tracked objects, since object motion implies motion. Segments without any motion are discarded even if they fall within the pre/post capture range.
- **`mode: active_objects`**: Only retains segments within the capture window where tracked objects were actively moving. Segments with general motion but no active objects are discarded.
+- **`mode: all`** — Retains every segment within the capture window, regardless of whether motion was detected.
+- **`mode: motion`** (default) — Only retains segments within the capture window that contain motion. This includes segments with active tracked objects, since object motion implies motion. Segments without any motion are discarded even if they fall within the pre/post capture range.
+- **`mode: active_objects`** — Only retains segments within the capture window where tracked objects were actively moving. Segments with general motion but no active objects are discarded.

 This means that with the default `motion` mode, you may see less footage than the configured pre/post capture duration if parts of the capture window had no motion.

@@ -197,7 +191,11 @@ Because recording segments are written in 10 second chunks, pre-capture timing d

 ### Where to view pre/post capture footage

-Pre and post capture footage is included in the **recording timeline**, visible in the History view. Note that pre/post capture settings only affect which recording segments are **retained on disk**. They do not change the start and end points shown in the UI. The History view will still center on the review item's actual time range, but you can scrub backward and forward through the retained pre/post capture footage on the timeline. The Explore view shows object-specific clips that are trimmed to when the tracked object was actually visible, so pre/post capture time will not be reflected there.
+Pre and post capture footage is included in the **recording timeline**, visible in the History view. Note that pre/post capture settings only affect which recording segments are **retained on disk** — they do not change the start and end points shown in the UI. The History view will still center on the review item's actual time range, but you can scrub backward and forward through the retained pre/post capture footage on the timeline. The Explore view shows object-specific clips that are trimmed to when the tracked object was actually visible, so pre/post capture time will not be reflected there.
+
+## Will Frigate delete old recordings if my storage runs out?
+
+If there is less than an hour left of storage, the oldest hour of recordings will be deleted and a message will be printed in the Frigate logs. This emergency cleanup deletes the oldest recordings first regardless of retention settings to reclaim space as quickly as possible.

 ## Configuring Recording Retention

@@ -351,63 +349,3 @@ Setting `verbose: true` writes a detailed report of every orphaned file and data
 This operation uses considerable CPU resources and includes a safety threshold that aborts if more than 50% of files would be deleted. Only run when necessary. If you set `force: true` the safety threshold will be bypassed; do not use `force` unless you are certain the deletions are intended.

 :::
-
-## Understanding storage usage
-
-The storage usage Frigate reports will not exactly match what the operating system reports with `df` or `du`. This is expected, not a bug. The sections below explain how Frigate derives its storage figures and why they differ from the disk's own accounting.
-
-### How Frigate measures recording usage
-
-The **Recordings** value on the Storage Metrics page (<NavPath path="System > Storage" />), and the per-camera **Camera Storage** breakdown, is the sum of the recording segment sizes Frigate has written, taken from Frigate's database. It is **not** computed by a scan of the disk. Frigate tracks usage this way by design: repeatedly walking the entire drive to total its size would keep hard drives spun up and add unnecessary I/O.
-
-The disk **total** shown beside it, and the free-space figure Frigate uses to decide when to delete recordings, instead come from the operating system's report for the whole filesystem mounted at `/media/frigate`. As a result, the **Unused** value on the page is _total disk capacity minus Frigate's recordings_, not the drive's real free space, which will be lower whenever anything else is stored on the disk.
-
-### What counts toward usage, and why it won't match `df`
-
-Only **recording segments** (`/media/frigate/recordings`) are included in the recordings storage total. Plenty of other things consume real disk space but are **not** part of that number:
-
- **Snapshots and thumbnails** (`/media/frigate/clips`): see [Snapshots](/configuration/snapshots). These are retained independently of recordings.
- **Preview videos** and **review thumbnails** (also under `/media/frigate/clips`).
- **Exports** (`/media/frigate/exports`): exports are never removed by retention.
- **The database, downloaded detection models, and face / license plate training images** (stored under `/config`).
- **Debug images from enrichments** (`/media/frigate/clips`): when enabled, License Plate Recognition's `debug_save_plates` and GenAI's `debug_save_thumbnails` save plate crops and request images for troubleshooting.
-
-These files are the usual explanation for an "other" or seemingly unaccounted bucket of space: it is real, it is Frigate's, and it simply isn't part of the _recordings_ total. They are also why comparing the **Recordings** figure to `df -h` always shows a gap: `df` additionally counts any non-Frigate data on the disk, filesystem overhead and reserved blocks (ext4 reserves ~5% for root by default, so a disk can read "full" before recordings approach the total), and recently deleted recordings whose space has not yet been reclaimed.
-
-:::tip
-
-The Storage page is not intended to be a system-wide disk monitor: it shows how much space _Frigate's recordings_ use. To see true disk usage, use `df -h` (free space) and `du -sh` (per-directory usage) on the host.
-
-:::
-
-### Free space and the `/media/frigate` mount
-
-Frigate reports the capacity and free space of whatever filesystem is actually mounted at `/media/frigate` **inside the container**. If an external drive or network share isn't truly mounted there (a missing `/etc/fstab` entry, a share that was offline when the container started, or a host that doesn't pass the path through), the container falls back to the host's OS disk, and Frigate will correctly report that smaller disk instead of the drive you intended.
-
-If the reported capacity doesn't match your drive, the mount is the place to look, not Frigate. Verify what is actually mounted from inside the container:
-
-```bash
-docker exec -it frigate df -h /media/frigate
-docker exec -it frigate mount | grep media
-```
-
-See the [storage mount layout](/frigate/installation#storage) for how the volumes are expected to be configured.
-
-### The `/tmp/cache` area is separate
-
-Recording segments are first written to `/tmp/cache`, a small, in-memory (`tmpfs`) area, before being checked and moved to `/media/frigate/recordings`. Because it is separate and small, `/tmp/cache` can fill up and produce `No space left on device` errors even when the recordings disk has plenty of room. They are different storage areas. See [Recordings troubleshooting](/troubleshooting/recordings) for diagnosing cache and slow-storage issues.
-
-### When the metrics don't match what's on disk
-
-Because usage is tracked in the database, deleting recording files directly on disk, or files left behind after an upgrade, will not update the reported usage, and can even push it above 100%. Frigate is unaware of files it didn't record and won't count or remove them automatically. Use [Syncing Media Files With Disk](#syncing-media-files-with-disk) to reconcile the database with what is actually on disk.
-
-## Will Frigate delete old recordings if my storage runs out?
-
-Yes. Frigate continuously checks the **free space of the disk** holding `/media/frigate/recordings`. This is different from adding up the size of every recording: free space is a single number the operating system already tracks, so Frigate can ask for it instantly without reading through your files or spinning up the disk, which is exactly why it relies on this check rather than scanning the drive. When less than roughly one hour of recording space remains (estimated from the current recording bitrate, **not** a fixed percentage), Frigate deletes the oldest recordings to reclaim space and logs a message. This emergency cleanup removes the oldest recordings first **regardless of retention settings**.
-
-Two consequences follow from this being based on whole-disk free space:
-
- Because the check uses the disk's real free space, **anything** filling the drive, including non-Frigate files, can trigger deletion of your oldest recordings.
- Cleanup can run while a meaningful percentage of the disk is still free (for example, with high bitrates or many cameras), because the threshold is "less than ~1 hour of recording headroom," not "X% full."
-
-Frequent emergency cleanups usually mean your configured retention exceeds what the disk can hold. Reduce your retention days so the normal retention cleanup keeps up and the emergency path rarely triggers.
@@ -147,13 +147,6 @@ auth:
  # NOTE: changing this value will not automatically update password hashes, you
  #       will need to change each user password for it to apply
  hash_iterations: 600000
-  # Optional: Map roles to the list of cameras each role can access (default: none)
-  # NOTE: An empty list grants the role access to all cameras. Roles defined here can be
-  #       referenced by proxy header role mapping or assigned to native users.
-  roles:
-    my_custom_role:
-      - front_door
-      - back_yard

 # Optional: model modifications
 # NOTE: The default values are for the EdgeTPU detector.
@@ -173,9 +166,6 @@ model:
  # Required: Object detection model input tensor format
  # Valid values are nhwc or nchw (default: shown below)
  input_tensor: nhwc
-  # Optional: Data type of the model input tensor
-  # Valid values are float, float_denorm, or int (default: shown below)
-  input_dtype: int
  # Required: Object detection model type, currently only used with the OpenVINO detector
  # Valid values are ssd, yolox, yolonas (default: shown below)
  model_type: ssd
@@ -206,12 +196,11 @@ audio:
  #  - 500 - medium sensitivity
  #  - 1000 - low sensitivity
  min_volume: 500
-  # Optional: Number of threads to use for audio detection (default: shown below)
-  num_threads: 2
  # Optional: Types of audio to listen for (default: shown below)
  listen:
    - bark
    - fire_alarm
+    - scream
    - speech
    - yell
  # Optional: Filters to configure detection.
@@ -268,7 +257,7 @@ birdseye:
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
  # Optional: ffmpeg binary path (default: shown below)
-  # can also be set to `8.0` or `5.0` to specify one of the included versions
+  # can also be set to `7.0` or `5.0` to specify one of the included versions
  # or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
  path: "default"
  # Optional: global ffmpeg args (default: shown below)
@@ -339,7 +328,7 @@ detect:
  # especially when using separate streams for detect and record.
  # Use this setting to make the timeline bounding boxes more closely align
  # with the recording. The value can be positive or negative.
-  # TIP: Imagine there is a tracked object clip with a person walking from left to right.
+  # TIP: Imagine there is an tracked object clip with a person walking from left to right.
  #      If the tracked object lifecycle bounding box is consistently to the left of the person
  #      then the value should be decreased. Similarly, if a person is walking from
  #      left to right and the bounding box is consistently ahead of the person
@@ -480,8 +469,6 @@ review:
      - Animals in the garden
    # Optional: Preferred response language (default: English)
    preferred_language: English
-    # Optional: Save thumbnails sent to the GenAI provider for review/debugging purposes (default: shown below)
-    debug_save_thumbnails: False

 # Optional: Motion configuration
 # NOTE: Can be overridden at the camera level
@@ -513,8 +500,6 @@ motion:
  #  - 30 - medium sensitivity
  #  - 50 - low sensitivity
  contour_area: 10
-  # Optional: Alpha blending factor used in frame differencing for motion calculation (default: shown below)
-  delta_alpha: 0.2
  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
  # Low values will cause things like moving shadows to be detected as motion for longer.
@@ -587,8 +572,6 @@ record:
    timelapse_args: "-vf setpts=0.04*PTS -r 30"
    # Optional: Global hardware acceleration settings for timelapse exports. (default: inherit)
    hwaccel_args: auto
-    # Optional: Maximum number of export jobs to process at the same time (default: shown below)
-    max_concurrent: 3
  # Optional: Recording Preview Settings
  preview:
    # Optional: Quality of recording preview (default: shown below).
@@ -731,42 +714,28 @@ lpr:
  enhancement: 0
  # Optional: Save plate images to /media/frigate/clips/lpr for debugging purposes (default: shown below)
  debug_save_plates: False
-  # Optional: List of regex replacement rules to normalize detected plates before matching (default: none)
-  replace_rules:
-    # Required: regex pattern to match in the detected plate
-    - pattern: "O"
-      # Required: string to replace the matched pattern with
-      replacement: "0"
+  # Optional: List of regex replacement rules to normalize detected plates (default: shown below)
+  replace_rules: {}

-# Optional: Configuration for AI / LLM providers
+# Optional: Configuration for AI / LLM provider
 # WARNING: Depending on the provider, this will send thumbnails over the internet
 # to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
 # the camera level to enhance privacy for indoor cameras.
-# NOTE: genai is a map of named providers. Each key is a name you choose for the provider,
-#       and each role (chat, descriptions, embeddings) may be assigned to exactly one provider.
 genai:
-  # Required: name of the provider (chosen by you, used to reference it elsewhere)
-  my_provider:
-    # Required: Provider must be one of ollama, openai, azure_openai, gemini, or llamacpp
-    provider: ollama
-    # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
-    base_url: http://localhost::11434
-    # Required if gemini or openai
-    api_key: "{FRIGATE_GENAI_API_KEY}"
-    # Required: The model to use with the provider.
-    model: gemini-1.5-flash
-    # Optional: Roles this provider handles (default: shown below)
-    # Each role (chat, descriptions, embeddings) must be assigned to exactly one provider.
-    roles:
-      - chat
-      - descriptions
-      - embeddings
-    # Optional additional args to pass to the GenAI Provider (default: None)
-    provider_options:
-      keep_alive: -1
-    # Optional: Options to pass during inference calls (default: {})
-    runtime_options:
-      temperature: 0.7
+  # Required: Provider must be one of ollama, gemini, or openai
+  provider: ollama
+  # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
+  base_url: http://localhost::11434
+  # Required if gemini or openai
+  api_key: "{FRIGATE_GENAI_API_KEY}"
+  # Required: The model to use with the provider.
+  model: gemini-1.5-flash
+  # Optional additional args to pass to the GenAI Provider (default: None)
+  provider_options:
+    keep_alive: -1
+  # Optional: Options to pass during inference calls (default: {})
+  runtime_options:
+    temperature: 0.7

 # Optional: Configuration for audio transcription
 # NOTE: only the enabled option can be overridden at the camera level
@@ -820,7 +789,7 @@ classification:
        interval: None

 # Optional: Restream configuration
-# Uses https://github.com/AlexxIT/go2rtc (v1.9.14)
+# Uses https://github.com/AlexxIT/go2rtc (v1.9.13)
 # NOTE: The default go2rtc API port (1984) must be used,
 #       changing this port for the integrated go2rtc instance is not supported.
 go2rtc:
@@ -871,8 +840,8 @@ cameras:
  # Required: name of the camera
  back:
    # Optional: Enable/Disable the camera (default: shown below).
-    # When False, ffmpeg is not started and the camera is hidden from the UI
-    # (except Camera Management). Re-enabling requires a Frigate restart.
+    # If disabled: config is used but no live stream and no capture etc.
+    # Events/Recordings are still viewable.
    enabled: True
    # Optional: camera type used for some Frigate features (default: shown below)
    # Options are "generic" and "lpr"
@@ -939,9 +908,6 @@ cameras:
        inertia: 3
        # Optional: Number of seconds that an object must loiter to be considered in the zone (default: shown below)
        loitering_time: 0
-        # Optional: Minimum speed required for an object to be considered present in the zone (default: none)
-        # In real-world units if distances are set. Used for speed-based zone triggers.
-        speed_threshold: 2.5
        # Optional: List of objects that can trigger this zone (default: all tracked objects)
        objects:
          - person
@@ -979,9 +945,6 @@ cameras:
      order: 0
      # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
      dashboard: True
-      # Optional: Whether this camera is visible in review (the review page and its camera
-      # filter, motion review, and the history view) (default: shown below)
-      review: True

    # Optional: connect to ONVIF camera
    # to enable PTZ controls.
@@ -1120,6 +1083,22 @@ ui:
  # Optional: Set the time format used.
  # Options are browser, 12hour, or 24hour (default: shown below)
  time_format: browser
+  # Optional: Set the date style for a specified length.
+  # Options are: full, long, medium, short
+  # Examples:
+  #    short: 2/11/23
+  #    medium: Feb 11, 2023
+  #    full: Saturday, February 11, 2023
+  # (default: shown below).
+  date_style: short
+  # Optional: Set the time style for a specified length.
+  # Options are: full, long, medium, short
+  # Examples:
+  #    short: 8:14 PM
+  #    medium: 8:15:22 PM
+  #    full: 8:15:22 PM Mountain Standard Time
+  # (default: shown below).
+  time_style: medium
  # Optional: Set the unit system to either "imperial" or "metric" (default: metric)
  # Used in the UI and in MQTT topics
  unit_system: metric
@@ -11,7 +11,7 @@ import NavPath from "@site/src/components/NavPath";

 Frigate can restream your video feed as an RTSP feed for other applications such as Home Assistant to utilize it at `rtsp://<frigate_host>:8554/<camera_name>`. Port 8554 must be open. [This allows you to use a video feed for detection in Frigate and Home Assistant live view at the same time without having to make two separate connections to the camera](#reduce-connections-to-camera). The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.

-Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.14) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#configuration) for more advanced configurations and features.
+Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.13) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#configuration) for more advanced configurations and features.

 :::note

@@ -61,7 +61,7 @@ Configure the go2rtc stream and point the camera inputs at the local restream.
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera. Then navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> for each camera. For each input, choose **Restream (go2rtc)** and pick the matching stream from the dropdown. Frigate uses the local restream URL (`rtsp://127.0.0.1:8554/<camera_name>`) and the `preset-rtsp-restream` input args for that input automatically. (Choose **Manual input path** instead to type a URL directly.)
+Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and set the input paths to use the local restream URL (`rtsp://127.0.0.1:8554/<camera_name>`).

 </TabItem>
 <TabItem value="yaml">
@@ -111,7 +111,7 @@ Two connections are made to the camera. One for the sub stream, one for the rest
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera and its sub stream. Then navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> for each camera and add separate inputs for the main and sub streams. Set each input's source to **Restream (go2rtc)** and pick the matching stream from the dropdown. Frigate uses the local restream URL and the `preset-rtsp-restream` input args for that input automatically.
+Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera and its sub stream. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and configure separate inputs for the main and sub streams using the local restream URLs.

 </TabItem>
 <TabItem value="yaml">
@@ -236,7 +236,7 @@ Enabling arbitrary exec sources allows execution of arbitrary commands through g

 ## Advanced Restream Configurations

-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:

 :::warning

@@ -23,7 +23,7 @@ In 0.14 and later, all of that is bundled into a single review item which starts

 ## Alerts and Detections

-Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring [required zones](/configuration/zones#restricting-alerts-and-detections-to-specific-zones) for them.
+Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.

 :::note

@@ -130,7 +130,3 @@ By default a review item will be created if any `review -> alerts -> labels` and
 Because zones don't apply to audio, audio labels will always be marked as a detection by default.

 :::
-
-## Reviewing Motion
-
-The Review page can also surface periods of motion that didn't produce a tracked object, and lets you search past recordings for motion in a region you draw. See [Reviewing Motion](/usage/review#reviewing-motion) in the Usage docs for how to use **Motion Previews** and **Motion Search**, and [Tuning Motion Detection](motion_detection.md) for configuring the underlying motion detector.
@@ -7,7 +7,7 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";

-Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_, numerical vector representations, for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
+Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.

 Frigate uses models from [Jina AI](https://huggingface.co/jinaai) to create and save embeddings to Frigate's database. All of this runs locally.

@@ -222,11 +222,16 @@ See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_

 ## Usage and Best Practices

-For tips on getting the best results from Semantic Search (choosing between thumbnail and description search, phrasing queries effectively, and combining search with the other Explore filters), see [Usage and best practices](/usage/explore#usage-and-best-practices) in the Usage docs.
+1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
+2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
+3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
+4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
+5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
+6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.

 ## Triggers

-Triggers utilize Semantic Search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.
+Triggers utilize Semantic Search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes a specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.

 :::note

@@ -7,17 +7,13 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";

-A snapshot is a single still image that captures a tracked object at its best moment: the clearest frame Frigate saw while following that object across the scene. Unlike a [recording](./record.md), which is continuous video, a snapshot is one representative image saved per tracked object once tracking ends.
+Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>-clean.webp`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)

-When snapshots are enabled, Frigate saves one image to `/media/frigate/clips` for each tracked object, named `<camera>-<id>-clean.webp`. A clean image is always stored without any annotations (no timestamp, bounding boxes, or cropping) so you have an unmodified copy of the original frame. Annotations like bounding boxes and timestamps are applied on demand when a snapshot is requested [via the HTTP API](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx). See [Rendering](#rendering) below.
+Snapshots are accessible in the UI in the Explore pane. This allows for quick submission to the Frigate+ service.

-A few things to keep in mind:
+To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones)

- Snapshots are saved per tracked object, so a camera with no detected objects produces no snapshots even if recording is enabled.
- Snapshots and recordings are configured and retained independently. Enabling one does not enable the other.
- Snapshots are accessible in the UI in the Explore pane, which allows for quick submission to the Frigate+ service.
- To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones).
- Snapshots sent via MQTT are configured separately under the camera MQTT settings, not here.
+Snapshots sent via MQTT are configured separately under the camera MQTT settings, not here.

 ## Enabling Snapshots

@@ -111,6 +107,7 @@ Navigate to <NavPath path="Settings > Global configuration > Snapshots" />.
 | Field                                              | Description                                                                         |
 | -------------------------------------------------- | ----------------------------------------------------------------------------------- |
 | **Snapshot retention > Default retention**         | Number of days to retain snapshots (default: 10)                                    |
+| **Snapshot retention > Retention mode**            | Retention mode: `all`, `motion`, or `active_objects`                                |
 | **Snapshot retention > Object retention > Person** | Per-object overrides for retention days (e.g., keep `person` snapshots for 15 days) |

 </TabItem>
@@ -121,6 +118,7 @@ snapshots:
  enabled: True
  retain:
    default: 10
+    mode: motion
    objects:
      person: 15
 ```
@@ -132,7 +130,7 @@ snapshots:

 Frigate does not save every frame. It picks a single "best" frame for each tracked object based on detection confidence, object size, and the presence of key attributes like faces or license plates. Frames where the object touches the edge of the frame are deprioritized. That best frame is written to disk once tracking ends.

-MQTT snapshots are published more frequently: each time a better thumbnail frame is found during tracking, or when the current best image is older than `best_image_timeout` (default: 60s). These use their own annotation settings configured under the camera MQTT settings.
+MQTT snapshots are published more frequently — each time a better thumbnail frame is found during tracking, or when the current best image is older than `best_image_timeout` (default: 60s). These use their own annotation settings configured under the camera MQTT settings.

 ## Rendering

@@ -43,7 +43,7 @@ Let's look at an example use case: I want to record any cars that enter my drive

 One might simply think "Why not just run object detection any time there is motion around the driveway area and notify if the bounding box is in that zone?"

-With that approach, what video is related to the car that entered the driveway? Did it come from the left or right? Was it parked across the street for an hour before turning into the driveway? One approach is to just record 24/7 or for motion (on any changed pixels) and not attempt to do that at all. This is what most other NVRs do. Just don't even try to identify a start and end for that object since it's hard and you will be wrong some portion of the time.
+With that approach, what video is related to the car that entered the driveway? Did it come from the left or right? Was it parked across the street for an hour before turning into the driveway? One approach is to just record 24/7 or for motion (on any changed changed pixels) and not attempt to do that at all. This is what most other NVRs do. Just don't even try to identify a start and end for that object since it's hard and you will be wrong some portion of the time.

 Couldn't you just look at when motion stopped and started? Motion for a video feed is nothing more than looking for pixels that are different than they were in previous frames. If the car entered the driveway while someone was mowing the grass, how would you know which motion was for the car and which was for the person when they mow along the driveway or street? What if another car was driving the other direction on the street? Or what if its a windy day and the bush by your mailbox is blowing around?

@@ -61,4 +61,4 @@ Now you have to determine which of the bounding boxes in this frame should be ma

 Now let's assume that those other 3 cars were already being tracked as stationary objects, so the car driving down the street is a new 4th car. The object tracker knows we have had 3 cars and we now have 4. As the new car approaches the parked cars, the bounding boxes for all 4 cars is predicted based on the previous frames. The predicted boxes for the parked cars is pretty much a 100% overlap with the bounding boxes in the new frame. The parked cars are slam dunk matches to the tracking ids they had before and the only one left is the remaining bounding box which gets assigned to the new car. This results in a much lower error rate. Not perfect, but better.

-The most difficult scenario that causes IDs to be assigned incorrectly is when an object completely occludes another object. When a car drives in front of another car and it's no longer visible, a bounding box disappeared and it's a bit of a toss up when assigning the id since it's difficult to know which one is in front of the other. This happens for cars passing in front of other cars fairly often. It's something that we want to improve in the future.
+The most difficult scenario that causes IDs to be assigned incorrectly is when an object completely occludes another object. When a car drives in front of another car and its no longer visible, a bounding box disappeared and it's a bit of a toss up when assigning the id since it's difficult to know which one is in front of the other. This happens for cars passing in front of other cars fairly often. It's something that we want to improve in the future.
@@ -18,7 +18,7 @@ Zones cannot have the same name as a camera. If desired, a single zone can inclu

 Zones can be toggled on or off without removing them from the configuration. Disabled zones are completely ignored at runtime - objects will not be tracked for zone presence, and zones will not appear in the debug view. This is useful for temporarily disabling a zone during certain seasons or times of day without modifying the configuration.

-During testing, enable the Zones option for the [Debug view](/usage/live#the-single-camera-view) of your camera so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
+During testing, enable the Zones option for the Debug view of your camera (Settings --> Debug) so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.

 ## Creating a Zone

@@ -61,7 +61,7 @@ Navigate to <NavPath path="Settings > Camera configuration > Review" />.

 | Field                              | Description                                                                               |
 | ---------------------------------- | ----------------------------------------------------------------------------------------- |
-| **Alerts config > Required zones** | Set to `entire_yard` so an object must enter that zone to be considered an alert; leave empty to allow alerts anywhere in the frame. |
+| **Alerts config > Required zones** | Zones that an object must enter to be considered an alert; leave empty to allow any zone. |

 </TabItem>
 <TabItem value="yaml">
@@ -82,7 +82,7 @@ cameras:
 </TabItem>
 </ConfigTabs>

-You may also want to filter detections to only be created when an object enters a secondary area of interest. For example, to trigger alerts when an object enters the inner area of the yard (an `inner_yard` zone) but detections when an object enters the edge of the yard (an `edge_yard` zone):
+You may also want to filter detections to only be created when an object enters a secondary area of interest. For example, to trigger alerts when an object enters the inner area of the yard but detections when an object enters the edge of the yard:

 <ConfigTabs>
 <TabItem value="ui">
@@ -91,8 +91,8 @@ Navigate to <NavPath path="Settings > Camera configuration > Review" />.

 | Field                                  | Description                                                                                  |
 | -------------------------------------- | -------------------------------------------------------------------------------------------- |
-| **Alerts config > Required zones**     | Set to `inner_yard` so an object must enter that zone to be considered an alert; leave empty to allow alerts anywhere in the frame.       |
-| **Detections config > Required zones** | Set to `edge_yard` so an object must enter that zone to be considered a detection; leave empty to allow detections anywhere in the frame. |
+| **Alerts config > Required zones**     | Zones that an object must enter to be considered an alert; leave empty to allow any zone.    |
+| **Detections config > Required zones** | Zones that an object must enter to be considered a detection; leave empty to allow any zone. |

 </TabItem>
 <TabItem value="yaml">
@@ -121,7 +121,7 @@ cameras:

 ### Restricting snapshots to specific zones

-To only save snapshots when an object enters a specific zone, for example an `entire_yard` zone:
+To only save snapshots when an object enters a specific zone:

 <ConfigTabs>
 <TabItem value="ui">
@@ -5,7 +5,7 @@ title: Camera setup

 Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Firefox 134+/136+/137+ (Windows/Mac/Linux & Android), Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.

- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The default frame rate of 5fps is correct for almost all cameras and rarely needs to be changed; see [Choosing a detect frame rate](#choosing-a-detect-frame-rate). Higher resolutions and frame rates will drive higher CPU usage on your server.
+- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher (10fps is the recommended maximum for most users) for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.

 - **Recording**: This stream should be the resolution you wish to store for reference. Typically, this will be the highest resolution your camera supports. I recommend setting this feed in your camera's firmware to 15 fps.

@@ -25,44 +25,6 @@ Larger resolutions **do** improve performance if the objects are very small in t

 ![Resolutions](/img/resolutions-min.jpg)

-### Choosing a detect frame rate
-
-`detect.fps` controls how many times per second Frigate runs object detection. It does **not** need to match your camera's frame rate. The default of **5** is correct for the vast majority of cameras.
-
-:::warning
-
-Most users who raise `detect.fps` above the default don't need to. Increasing it consumes more CPU/GPU (detection load scales directly with the frame rate) while providing **no benefit to tracking** once objects are already being followed smoothly. Leave it at **5** unless you have a specific scene that fails the test below, and confirm any change actually helps in the [debug view](/usage/live#the-single-camera-view).
-
-:::
-
-#### Why 5 is enough for almost everyone
-
-Frigate follows an object by matching its bounding box from one detection frame to the next, which requires the object to be detected often enough while it is on screen. At 5 fps this is satisfied in normal scenes: an object crossing a yard, porch, driveway, or walkway is in view for several seconds and produces ~15 or more detections, which is more than enough for a reliable track and a good snapshot. This includes fast subjects such as a running person or a bolting pet, which on a wide-angle view remain on screen for several seconds.
-
-A higher rate helps only when an object crosses the **entire frame in less than two seconds**, which is determined by camera framing rather than object speed - for example, a camera aimed down a street at fast cross-traffic. In those scenes 5 fps may produce too few detections to hold a track. Cameras covering normal approaches and open areas are unaffected.
-
-#### Checking whether a higher rate is needed
-
-Estimate how long an object is visible as it crosses the area of interest, aiming for roughly 8–10 detections during the pass:
-
-> **`detect.fps` ≈ 10 ÷ (seconds the object is in view)**
-
-Most objects (people walking or running, pets, and vehicles in a yard, driveway, or walkway) stay in view for two seconds or more, so the default of 5 fps is correct. Slowly try raising it to 10 (the recommended maximum) in increments only when objects routinely cross the entire frame in about a second, such as a camera aimed at a street or sidewalk with fast cross-traffic. Objects that transit in under a second cannot be tracked reliably at any practical rate, so reposition the camera instead.
-
-:::tip
-
-If the formula calls for more than 10, the fix is **camera placement, not frame rate**. Angle the camera so objects move toward it rather than across the view, or aim it where traffic slows. A higher `detect.fps` increases CPU load proportionally without producing more detections of a too-brief object.
-
-:::
-
-#### Verify in the debug view
-
-Confirm any change in the Debug view or Debug Replay. Watch a typical object cross the scene: if its bounding box follows it smoothly while visible, the rate is sufficient. A box that jumps erratically, drops out, or splits one object into multiple events indicates the rate should be increased one step.
-
-#### Dedicated LPR cameras
-
-A dedicated license plate recognition camera is the most common reason to use something higher than 5 fps: the camera is highly zoomed, the plate is small, and it moves at full vehicle speed, so it transits the frame quickly. However, the same ceiling applies: above 10 fps is unnecessary, and **placement matters most**: aim LPR cameras where vehicles slow down, such as gates, driveways, and parking entrances. A tight view of a fast through-road will not likely read plates reliably at any frame rate. See [License Plate Recognition](/configuration/license_plate_recognition) for details.
-
 ### Example Camera Configuration

 For the Dahua/Loryta 5442 camera, I use the following settings:
@@ -5,40 +5,20 @@ title: Glossary

 The glossary explains terms commonly used in Frigate's documentation.

-## Alert
-
-The higher-priority of the two [review item](#review-item) severities, the other being a [detection](#detection). By default a review item is an alert when it involves a `person` or `car`; the qualifying [labels](#label) and [zones](#zone) can be configured. [See the review docs for more info](/configuration/review)
-
-## Attribute
-
-A property detected on an [object](#object) that exists alongside its [label](#label). Unlike a [sub label](#sub-label), an object can carry several attributes at once. Some attributes come directly from the object detection [model](#model) (for example `face`, `license_plate`, or delivery carrier logos such as `amazon`, `ups`, and `fedex`), while others come from a [custom object classification model](/configuration/custom_classification/object_classification) configured with the `attribute` type. Attributes are visible in the Tracked Object Details pane in Explore, in `frigate/events` MQTT messages, and through the HTTP API.
-
 ## Bounding Box

-A box returned by the object detection [model](#model) that outlines a detected [object](#object) in the frame. In the [Debug view](/usage/live#the-single-camera-view), bounding boxes are colored by object [label](#label).
+A box returned from the object detection model that outlines an object in the frame. These have multiple colors depending on object type in the debug live view.

 ### Bounding Box Colors

 - At startup different colors will be assigned to each object label
 - A dark blue thin line indicates that object is not detected at this current point in time
 - A gray thin line indicates that object is detected as being stationary
- A thick line indicates that object is the subject of autotracking (when enabled)
-
-## Class
-
-The categories a classification [model](#model) is trained to distinguish between. Each class is a distinct visual category the model predicts, plus a `none` class for inputs that don't fit any category. For example, a custom object classification model for `person` objects might use the classes `delivery_person`, `resident`, and `none`. The predicted class is applied to the [object](#object) as either a [sub label](#sub-label) or an [attribute](#attribute), depending on the model's configuration. [See the object classification docs for more info](/configuration/custom_classification/object_classification)
-
-## Detection
-
-The lower-priority of the two [review item](#review-item) severities, the other being an [alert](#alert). By default, any review item that does not qualify as an alert is a detection; the qualifying [labels](#label) and [zones](#zone) can be configured. Despite the name, a detection is a category of review item, not the same as the object detection performed by the [model](#model). [See the review docs for more info](/configuration/review)
+- A thick line indicates that object is the subject of autotracking (when enabled).

 ## False Positive

-An incorrect result from the object detection [model](#model), where it assigns the wrong [label](#label) to something in the frame, for example a dog identified as a person, or a chair identified as a dog. A person correctly identified in an area you want to ignore is not a false positive.
-
-## Label
-
-The type assigned to a detected [object](#object) by the object detection [model](#model), drawn from the model's labelmap, for example `person`, `car`, or `dog`. Frigate tracks `person` by default; additional labels are tracked by adding them to the objects configuration. [See the available objects docs for the full list](/configuration/objects)
+An incorrect detection of an object type. For example a dog being detected as a person, a chair being detected as a dog, etc. A person being detected in an area you want to ignore is not a false positive.

 ## Mask

@@ -46,56 +26,44 @@ There are two types of masks in Frigate. [See the mask docs for more info](/conf

 ### Motion Mask

-A motion mask stops [motion](#motion) in the masked area from triggering object detection. It does not stop an object from being detected when object detection runs because of motion in a nearby area. Use motion masks for parts of the frame that change constantly but never contain objects you care about: camera timestamps, the sky, the tops of trees, and so on.
+Motion masks prevent detection of [motion](#motion) in masked areas from triggering Frigate to run object detection, but do not prevent objects from being detected if object detection runs due to motion in nearby areas. For example: camera timestamps, skies, the tops of trees, etc.

 ### Object Mask

-An object filter mask drops any [bounding box](#bounding-box) whose bottom center falls inside the masked area (overlap elsewhere doesn't matter). The object is forced to be treated as a [false positive](#false-positive) and ignored.
+Object filter masks drop any bounding boxes where the bottom center (overlap doesn't matter) is in the masked area. It forces them to be considered a [false positive](#false-positive) so that they are ignored.

 ## Min Score

-The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
-
-## Model
-
-A machine learning model that Frigate uses to detect or classify objects. The object detection model locates [objects](#object) in each frame and returns their [labels](#label) and [bounding boxes](#bounding-box). Additional enrichment models run on tracked objects to add detail: face recognition, license plate recognition, bird classification, custom object and state classification, and the embedding models used for semantic search. [See the object detectors docs for more info](/configuration/object_detectors)
+The lowest score that an object can be detected with during tracking, any detection with a lower score will be assumed to be a false positive

 ## Motion

-A change in pixels between the current camera frame and previous frames. When many nearby pixels change together, they are grouped and shown as a red motion box in the debug live view. [See the motion detection docs for more info](/configuration/motion_detection)
-
-## Object
-
-Something Frigate can detect and follow in a camera frame, identified by its [label](#label) (for example a person or a car). The object types Frigate watches for are set in the `objects` configuration. Once an object is detected and followed across frames it becomes a [tracked object](#tracked-object-event-in-previous-versions), which may also carry a [sub label](#sub-label) and [attributes](#attribute). [See the available objects docs for more info](/configuration/objects)
+When pixels in the current camera frame are different than previous frames. When many nearby pixels are different in the current frame they grouped together and indicated with a red motion box in the live debug view. [See the motion detection docs for more info](/configuration/motion_detection)

 ## Region

-A portion of the camera frame sent to the object detection [model](#model). Regions are selected because of [motion](#motion), active objects, or occasionally to recheck stationary objects, and are shown as green boxes in the debug live view.
+A portion of the camera frame that is sent to object detection, regions can be sent due to motion, active objects, or occasionally for stationary objects. These are represented by green boxes in the debug live view.

 ## Review Item

-A period of time during which one or more [tracked objects](#tracked-object-event-in-previous-versions) were active, grouped together for review. Each review item is categorized as either an [alert](#alert) or a [detection](#detection). [See the review docs for more info](/configuration/review)
+A review item is a time period where any number of events/tracked objects were active. [See the review docs for more info](/configuration/review)

 ## Snapshot Score

-The object's score at the specific moment the snapshot was captured.
-
-## Sub Label
-
-A more specific identity assigned to a [tracked object](#tracked-object-event-in-previous-versions) in addition to its [label](#label). A `person` may get the name of a recognized face, a `car` may get the name of a known license plate, and a `bird` may get its species. An object can have only one sub label at a time. Sub labels are produced by face recognition, license plate recognition, bird classification, custom object classification configured with the `sub label` type, and semantic search triggers.
+The score shown in a snapshot is the score of that object at that specific moment in time.

 ## Threshold

-The median score an object must reach to be considered a true positive.
+The threshold is the median score that an object must reach in order to be considered a true positive.

 ## Top Score

-The highest median score an object reached over its lifetime.
+The top score for an object is the highest median score for an object.

 ## Tracked Object ("event" in previous versions)

-An [object](#object) followed from the moment it enters the frame until it leaves, including any time it stays still. A tracked object is saved once it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording.
+The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Tracked objects are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.

 ## Zone

-A user-defined area of interest within the camera frame. Zones can be used for notifications and to limit where Frigate creates a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
+Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
@@ -55,7 +55,7 @@ Frigate supports multiple different detectors that work on different types of ha
 **Most Hardware**

 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices offering a wide range of compatibility with devices.
-  - [Supports many model architectures](../../configuration/object_detectors#configuration-hailo)
+  - [Supports many model architectures](../../configuration/object_detectors#configuration)
  - Runs best with tiny or small size models

 - [Google Coral EdgeTPU](#google-coral-tpu): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
@@ -68,26 +68,26 @@ Frigate supports multiple different detectors that work on different types of ha
 **AMD**

 - [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
-  - [Supports limited model architectures](../../configuration/object_detectors#amdrocm-gpu-detector)
+  - [Supports limited model architectures](../../configuration/object_detectors#rocm-supported-models)
  - Runs best on discrete AMD GPUs

 **Apple Silicon**

 - [Apple Silicon](#apple-silicon): Apple Silicon is usable on all M1 and newer Apple Silicon devices to provide efficient and fast object detection
-  - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#apple-silicon-detector)
+  - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#apple-silicon-supported-models)
  - Runs well with any size models including large
  - Runs via ZMQ proxy which adds some latency, only recommended for local connection

 **Intel**

 - [OpenVino](#openvino---intel): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel NPUs to provide efficient object detection.
-  - [Supports majority of model architectures](../../configuration/object_detectors#openvino-detector)
+  - [Supports majority of model architectures](../../configuration/object_detectors#openvino-supported-models)
  - Runs best with tiny, small, or medium models

 **Nvidia**

 - [Nvidia GPU](#nvidia-gpus): Nvidia GPUs can provide efficient object detection.
-  - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx)
+  - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
  - Runs well with any size models including large

 - <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
@@ -111,14 +111,14 @@ Frigate supports multiple different detectors that work on different types of ha

 ### Hailo-8

-Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms, including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
+Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.

 **Default Model Configuration:**

 - **Hailo-8L:** Default model is **YOLOv6n**.
 - **Hailo-8:** Default model is **YOLOv6n**.

-In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms, with dual PCIe lanes, yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
+In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms—with dual PCIe lanes—yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.

 | Name             | Hailo‑8 Inference Time | Hailo‑8L Inference Time |
 | ---------------- | ---------------------- | ----------------------- |
@@ -94,7 +94,7 @@ The following sections contain additional setup steps that are only required if

 By default, the Raspberry Pi limits the amount of memory available to the GPU. In order to use ffmpeg hardware acceleration, you must increase the available memory by setting `gpu_mem` to the maximum recommended value in `config.txt` as described in the [official docs](https://www.raspberrypi.org/documentation/computers/config_txt.html#memory-options).

-Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with its own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
+Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).

 ### Hailo-8

@@ -600,7 +600,7 @@ There are several variants of the App available:

 If you are using hardware acceleration for ffmpeg, you **may** need to use the _Full Access_ variant of the App. This is because the Frigate App runs in a container with limited access to the host system. The _Full Access_ variant allows you to disable _Protection mode_ and give Frigate full access to the host system.

-You can also edit the Frigate configuration file through the [VS Code App](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](../configuration/config.md#accessing-app-config-dir).
+You can also edit the Frigate configuration file through the [VS Code App](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](../configuration/index.md#accessing-app-config-dir).

 ## Kubernetes

@@ -749,7 +749,7 @@ Failure to remap port 5000 on the host will result in the WebUI and all API endp

 :::

-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 negligible, 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)

@@ -768,7 +768,7 @@ services:
      - /path/to/your/recordings:/recordings
    ports:
      - "8971:8971"
-      # If exposing on macOS map to a different host port like 5001 or any other port with no conflicts
+      # 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.
      - "8554:8554" # RTSP feeds
    extra_hosts:
@@ -11,9 +11,9 @@ Frigate is designed to run locally and does not require a persistent internet co

 Frigate's internet usage falls into three categories:

-1. **One-time model downloads**: ML models are downloaded the first time a feature is enabled, then cached locally. No internet is needed on subsequent startups.
-2. **Optional cloud services**: Features like Frigate+ and Generative AI connect to external APIs only when explicitly configured.
-3. **Build-time dependencies**: Components bundled into the Docker image during the build process. These require no internet at runtime.
+1. **One-time model downloads** — ML models are downloaded the first time a feature is enabled, then cached locally. No internet is needed on subsequent startups.
+2. **Optional cloud services** — Features like Frigate+ and Generative AI connect to external APIs only when explicitly configured.
+3. **Build-time dependencies** — Components bundled into the Docker image during the build process. These require no internet at runtime.

 :::tip

@@ -32,7 +32,7 @@ The following models are downloaded automatically the first time their associate
 | [License plate recognition](/configuration/license_plate_recognition)                         | PaddleOCR (detection, classification, recognition) + YOLOv9 plate detector | GitHub               |
 | [Bird classification](/configuration/bird_classification)                                     | MobileNetV2 bird model + label map                                         | GitHub               |
 | [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
-| [Audio transcription](/configuration/advanced/system)                                         | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
+| [Audio transcription](/configuration/advanced)                                                | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |

 ### Hardware-Specific Detector Models

@@ -91,13 +91,13 @@ See [Frigate+](/integrations/plus) for details.

 When a Generative AI provider is configured, Frigate sends images and prompts to the configured provider for event descriptions, chat, and camera monitoring. Available providers:

-| Provider      | Internet Required                                               |
-| ------------- | --------------------------------------------------------------- |
-| OpenAI        | Yes, connects to OpenAI API (or custom base URL)                |
-| Google Gemini | Yes, connects to Google Generative AI API                       |
-| Azure OpenAI  | Yes, connects to your Azure endpoint                            |
-| Ollama        | Depends: typically local (`localhost:11434`), but can be remote |
-| llama.cpp     | No, runs entirely locally                                       |
+| Provider      | Internet Required                                                |
+| ------------- | ---------------------------------------------------------------- |
+| OpenAI        | Yes — connects to OpenAI API (or custom base URL)                |
+| Google Gemini | Yes — connects to Google Generative AI API                       |
+| Azure OpenAI  | Yes — connects to your Azure endpoint                            |
+| Ollama        | Depends — typically local (`localhost:11434`), but can be remote |
+| llama.cpp     | No — runs entirely locally                                       |

 Disable Generative AI by removing the `genai` configuration from your cameras. See [Generative AI](/configuration/genai/genai_config) for details.

@@ -126,30 +126,30 @@ When using the [DeepStack detector plugin](/configuration/object_detectors), Fri

 For [WebRTC live streaming](/configuration/live), Frigate uses STUN for NAT traversal:

- **go2rtc** defaults to a local STUN listener (`stun:8555`), no internet required.
+- **go2rtc** defaults to a local STUN listener (`stun:8555`) — no internet required.
 - **The web UI's WebRTC player** includes a fallback to Google's public STUN server (`stun:stun.l.google.com:19302`), which requires internet.

 ## Home Assistant Supervisor

-When running as a Home Assistant App, the go2rtc startup script queries the local Supervisor API (`http://supervisor/`) to discover the host IP address and WebRTC port. This is a local network call to the Home Assistant host, not an internet connection.
+When running as a Home Assistant add-on, the go2rtc startup script queries the local Supervisor API (`http://supervisor/`) to discover the host IP address and WebRTC port. This is a local network call to the Home Assistant host, not an internet connection.

 ## What Does NOT Require Internet

- **Object detection**: CPU, EdgeTPU, OpenVINO, and other bundled detector models are included in the Docker image.
- **Recording and playback**: All video is stored and served locally.
- **Live streaming**: Camera streams are pulled over your local network. MSE and HLS streaming work without any external connections.
- **The web interface**: Fully self-contained with no external fonts, scripts, analytics, or CDN dependencies. All translations are bundled locally.
- **Custom classification inference**: After training, custom models run entirely locally.
- **Audio detection**: The YAMNet audio classification model is bundled in the Docker image.
+- **Object detection** — CPU, EdgeTPU, OpenVINO, and other bundled detector models are included in the Docker image.
+- **Recording and playback** — All video is stored and served locally.
+- **Live streaming** — Camera streams are pulled over your local network. MSE and HLS streaming work without any external connections.
+- **The web interface** — Fully self-contained with no external fonts, scripts, analytics, or CDN dependencies. All translations are bundled locally.
+- **Custom classification inference** — After training, custom models run entirely locally.
+- **Audio detection** — The YAMNet audio classification model is bundled in the Docker image.

 ## Running Frigate Offline

 To run Frigate in an air-gapped or offline environment:

-1. **Pre-download models**: Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
-2. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
-3. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
-4. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
-5. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
+1. **Pre-download models** — Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
+2. **Disable version check** — Set `telemetry.version_check: false` in your configuration.
+3. **Block outbound model requests** — Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
+4. **Avoid cloud features** — Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
+5. **Use local model mirrors** — If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.

 After these steps, Frigate will operate with no outbound internet connections.
@@ -42,8 +42,6 @@ Frigate requires a CPU with AVX + AVX2 instructions. Most modern CPUs (post-2011

 Storage is an important consideration when planning a new installation. To get a more precise estimate of your storage requirements, you can use an IP camera storage calculator. Websites like [IPConfigure Storage Calculator](https://calculator.ipconfigure.com/) can help you determine the necessary disk space based on your camera settings.

-Once running, see [Understanding storage usage](/configuration/record#understanding-storage-usage) for how Frigate measures and reports disk usage, and why its numbers won't exactly match `df` or `du`.
-
 #### SSDs (Solid State Drives)

 SSDs are an excellent choice for Frigate, offering high speed and responsiveness. The older concern that SSDs would quickly "wear out" from constant video recording is largely no longer valid for modern consumer and enterprise-grade SSDs.
@@ -0,0 +1,116 @@
+---
+id: configuring_go2rtc
+title: Configuring go2rtc
+---
+
+Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect directly to your cameras. However, adding go2rtc to your configuration is required for the following features:
+
+- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
+- Live stream support for cameras in Home Assistant Integration
+- RTSP relay for use with other consumers to reduce the number of connections to your camera streams
+
+## Setup a go2rtc stream
+
+First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#module-streams), not just rtsp.
+
+:::tip
+
+For the best experience, you should set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
+
+See [the live view docs](../configuration/live.md#setting-streams-for-live-ui) for more information.
+
+:::
+
+```yaml
+go2rtc:
+  streams:
+    back:
+      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+```
+
+After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
+
+### What if my video doesn't play?
+
+- Check Logs:
+  - Access the go2rtc logs in the Frigate UI under Logs in the sidebar.
+  - If go2rtc is having difficulty connecting to your camera, you should see some error messages in the log.
+
+- Check go2rtc Web Interface: if you don't see any errors in the logs, try viewing the camera through go2rtc's web interface.
+  - Navigate to port 1984 in your browser to access go2rtc's web interface.
+    - If using Frigate through Home Assistant, enable the web interface at port 1984.
+    - If using Docker, forward port 1984 before accessing the web interface.
+  - Click `stream` for the specific camera to see if the camera's stream is being received.
+
+- Check Video Codec:
+  - If the camera stream works in go2rtc but not in your browser, the video codec might be unsupported.
+  - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#codecs-madness) in go2rtc documentation.
+  - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
+    ```yaml
+    go2rtc:
+      streams:
+        back:
+          - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          - "ffmpeg:back#video=h264#hardware"
+    ```
+
+- Switch to FFmpeg if needed:
+  - Some camera streams may need to use the ffmpeg module in go2rtc. This has the downside of slower startup times, but has compatibility with more stream types.
+
+    ```yaml
+    go2rtc:
+      streams:
+        back:
+          - ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+    ```
+
+  - If you can see the video but do not have audio, this is most likely because your camera's audio stream codec is not AAC.
+  - If possible, update your camera's audio settings to AAC in your camera's firmware.
+  - If your cameras do not support AAC audio, you will need to tell go2rtc to re-encode the audio to AAC on demand if you want audio. This will use additional CPU and add some latency. To add AAC audio on demand, you can update your go2rtc config as follows:
+
+    ```yaml
+    go2rtc:
+      streams:
+        back:
+          - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          - "ffmpeg:back#audio=aac"
+    ```
+
+    If you need to convert **both** the audio and video streams, you can use the following:
+
+    ```yaml
+    go2rtc:
+      streams:
+        back:
+          - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+          - "ffmpeg:back#video=h264#audio=aac#hardware"
+    ```
+
+    When using the ffmpeg module, you would add AAC audio like this:
+
+    ```yaml
+    go2rtc:
+      streams:
+        back:
+          - "ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#video=copy#audio=copy#audio=aac#hardware"
+    ```
+
+:::warning
+
+To access the go2rtc stream externally when utilizing the Frigate App (for
+instance through VLC), you must first enable the RTSP Restream port.
+You can do this by visiting the Frigate App configuration page within Home
+Assistant and revealing the hidden options under the "Show disabled ports"
+section.
+
+:::
+
+### Next steps
+
+1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
+2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
+3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
+
+## Homekit Configuration
+
+To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
@@ -144,7 +144,7 @@ At this point you should be able to start Frigate and a basic config will be cre

 ### Step 2: Add a camera

-Click the **Add Camera** button in <NavPath path="Settings > Global configuration > Camera management" /> to use the camera setup wizard to get your first camera added into Frigate.
+Click the **Add Camera** button in <NavPath path="Settings > Camera configuration > Management" /> to use the camera setup wizard to get your first camera added into Frigate.

 ### Step 3: Configure hardware acceleration (recommended)

@@ -301,11 +301,11 @@ cameras:

 More details on available detectors can be found [here](../configuration/object_detectors.md).

-Restart Frigate and you should start seeing detections for `person`. If you want to track other objects, they can be configured in <NavPath path="Settings > Global configuration > Objects" /> or via the [configuration file reference](../configuration/advanced/reference.md).
+Restart Frigate and you should start seeing detections for `person`. If you want to track other objects, they can be configured in <NavPath path="Settings > Global configuration > Objects" /> or via the [configuration file reference](../configuration/reference.md).

 ### Step 5: Setup motion masks

-Now that you have optimized your configuration for decoding the video stream, you will want to check to see where to implement motion masks. Click on the camera from the main dashboard, then select the gear icon in the top right, enable the [Debug view](/usage/live#the-single-camera-view), and finally enable the switch for Motion Boxes. Watch for areas that continuously trigger unwanted motion to be detected. Common areas to mask include camera timestamps and trees that frequently blow in the wind. The goal is to avoid wasting object detection cycles looking at these areas.
+Now that you have optimized your configuration for decoding the video stream, you will want to check to see where to implement motion masks. Click on the camera from the main dashboard, then select the gear icon in the top right, enable Debug View, and finally enable the switch for Motion Boxes. Watch for areas that continuously trigger unwanted motion to be detected. Common areas to mask include camera timestamps and trees that frequently blow in the wind. The goal is to avoid wasting object detection cycles looking at these areas.

 Use the mask editor to draw polygon masks directly on the camera feed. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and set up a motion mask over the area. More information about masks can be found [here](../configuration/masks.md).

@@ -348,7 +348,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
 <ConfigTabs>
 <TabItem value="ui">

-1. If you have separate streams for detect and record, navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" />, select your camera, and add a second input with the `record` role pointing to your high-resolution stream
+1. If you have separate streams for detect and record, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />, select your camera, and add a second input with the `record` role pointing to your high-resolution stream
 2. Navigate to <NavPath path="Settings > Global configuration > Recording" /> (or <NavPath path="Settings > Camera configuration > Recording" /> for a specific camera) and set **Enable recording** to on

 </TabItem>
@@ -388,20 +388,21 @@ If you only plan to use Frigate for recording, it is still recommended to define

 :::

-By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/advanced/reference.md).
+By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).

 ### Step 7: Complete config

 At this point you have a complete config with basic functionality.

- View [common configuration examples](../configuration/config.md#common-configuration-examples) for a list of common configuration examples.
- View [full config reference](../configuration/advanced/reference.md) for a complete list of configuration options.
+- View [common configuration examples](../configuration/index.md#common-configuration-examples) for a list of common configuration examples.
+- View [full config reference](../configuration/reference.md) for a complete list of configuration options.

 ### Follow up

 Now that you have a working install, you can use the following documentation for additional features:

-1. [Zones](../configuration/zones.md)
-2. [Review](../configuration/review.md)
-3. [Masks](../configuration/masks.md)
-4. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant
+1. [Configuring go2rtc](configuring_go2rtc.md) - Additional live view options and RTSP relay
+2. [Zones](../configuration/zones.md)
+3. [Review](../configuration/review.md)
+4. [Masks](../configuration/masks.md)
+5. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant
@@ -10,14 +10,13 @@ A reverse proxy is typically needed if you want to set up Frigate on a custom UR
 Before setting up a reverse proxy, check if any of the built-in functionality in Frigate suits your needs:
 |Topic|Docs|
 |-|-|
-|TLS|Please see the `tls` [configuration option](../configuration/tls.md)|
+|TLS|Please see the  `tls` [configuration option](../configuration/tls.md)|
 |Authentication|Please see the [authentication](../configuration/authentication.md) documentation|
-|IPv6|[Enabling IPv6](../configuration/advanced/system.md#enabling-ipv6)
+|IPv6|[Enabling IPv6](../configuration/advanced.md#enabling-ipv6)

-**Note about TLS**
-When using a reverse proxy, the TLS session is usually terminated at the proxy, sending the internal request over plain HTTP. If this is the desired behavior, TLS must first be disabled in Frigate, or you will encounter an HTTP 400 error: "The plain HTTP request was sent to HTTPS port."
+**Note about TLS**  
+When using a reverse proxy, the TLS session is usually terminated at the proxy, sending the internal request over plain HTTP. If this is the desired behavior, TLS must first be disabled in Frigate, or you will encounter an HTTP 400 error: "The plain HTTP request was sent to HTTPS port."  
 To disable TLS, set the following in your Frigate configuration:
-
 ```yml
 tls:
  enabled: false
@@ -25,26 +24,18 @@ tls:

 :::warning
 A reverse proxy can be used to secure access to an internal web server, but the user will be entirely reliant on the steps they have taken. You must ensure you are following security best practices.
-This page does not attempt to outline the specific steps needed to secure your internal website.
+This page does not attempt to outline the specific steps needed to secure your internal website.  
 Please use your own knowledge to assess and vet the reverse proxy software before you install anything on your system.
 :::

-## WebSocket support
-
-Frigate relies on WebSockets for real-time communication between the browser and the backend. Features such as camera controls (enabling/disabling a camera, audio, detect, recordings, and other toggles), live stream playback, and other live-updating parts of the UI will not function correctly if WebSocket connections are not proxied.
-
-Your reverse proxy must be configured to forward the `Upgrade` and `Connection` headers so that WebSocket connections can be established. Each proxy example below already includes the directives needed to do this, but if you are adapting your own configuration, ensure these headers are passed through.
-
-Note that some proxies disable WebSocket support by default. For example, Nginx Proxy Manager has a "Websockets Support" toggle that must be enabled.
-
 ## Proxies

 There are many solutions available to implement reverse proxies and the community is invited to help out documenting others through a contribution to this page.

- [Apache2](#apache2-reverse-proxy)
- [Nginx](#nginx-reverse-proxy)
- [Traefik](#traefik-reverse-proxy)
- [Caddy](#caddy-reverse-proxy)
+* [Apache2](#apache2-reverse-proxy)
+* [Nginx](#nginx-reverse-proxy)
+* [Traefik](#traefik-reverse-proxy)
+* [Caddy](#caddy-reverse-proxy)

 ## Apache2 Reverse Proxy

@@ -168,7 +159,7 @@ The settings below enabled connection upgrade, sets up logging (optional) and pr

 ## Traefik Reverse Proxy

-This example shows how to add a `label` to the Frigate Docker compose file, enabling Traefik to automatically discover your Frigate instance.
+This example shows how to add a `label` to the Frigate Docker compose file, enabling Traefik to automatically discover your Frigate instance.  
 Before using the example below, you must first set up Traefik with the [Docker provider](https://doc.traefik.io/traefik/providers/docker/)

 ```yml
@@ -212,7 +203,7 @@ This example shows Frigate running under a subdomain with logging and a tls cert
 }

 frigate.YOUR_DOMAIN.TLD {
-        reverse_proxy http://localhost:8971
+        reverse_proxy http://localhost:8971 
        import tls
        import logging frigate.YOUR_DOMAIN.TLD
 }
@@ -122,7 +122,7 @@ Use `http://<frigate_device_ip>:8971` as the URL for the integration so that aut

 The above URL assumes you have [disabled TLS](../configuration/tls).
 By default, TLS is enabled and Frigate will be using a self-signed certificate. HomeAssistant will fail to connect HTTPS to port 8971 since it fails to verify the self-signed certificate.
-Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be accessible with a valid certificate.
+Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be acessible with a valid certificate.

 :::

@@ -195,7 +195,7 @@ For clips to be castable to media devices, audio is required and may need to be

 ## Camera API

-To turn a camera off (pauses Frigate's processing of the stream; does not persist across Frigate restarts; see [Camera state](/configuration/live#camera-state)):
+To disable a camera dynamically

 ```
 action: camera.turn_off
@@ -204,7 +204,7 @@ target:
  entity_id: camera.back_deck_cam  # your Frigate camera entity ID
 ```

-To turn a camera back on:
+To enable a camera that has been disabled dynamically

 ```
 action: camera.turn_on
@@ -213,12 +213,6 @@ target:
  entity_id: camera.back_deck_cam  # your Frigate camera entity ID
 ```

-:::note
-
-These actions toggle Frigate's runtime On/Off state. To permanently disable a camera, set its status to **Disabled** in **Settings → Camera Management** in the Frigate UI.
-
-:::
-
 ## Notification API

 Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
@@ -280,7 +280,7 @@ Same data available at `/api/stats` published at a configurable interval.

 ### `frigate/camera_activity`

-Returns data about each camera, its current features, and if it is detecting motion, objects, etc. Can be triggered by publishing to `frigate/onConnect`
+Returns data about each camera, its current features, and if it is detecting motion, objects, etc. Can be triggered by publising to `frigate/onConnect`

 ### `frigate/profile/set`

@@ -306,7 +306,7 @@ Publishes the current health status of each role that is enabled (`audio`, `dete

 - `online`: Stream is running and being processed
 - `offline`: Stream is offline and is being restarted
- `disabled`: Camera is currently turned off (either at runtime via the `enabled/set` topic, or persistently via the configuration file). See [Camera state](/configuration/live#camera-state) for the distinction.
+- `disabled`: Camera is currently disabled

 ### `frigate/<camera_name>/<object_name>`

@@ -368,15 +368,15 @@ The published value is the detected state class name (e.g., `open`, `closed`, `o

 ### `frigate/<camera_name>/enabled/set`

-Topic to turn Frigate's processing of a camera on or off at runtime. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)). To permanently change the configured value, use **Settings → Global configuration → Camera management** in the Frigate UI. See [Camera state](/configuration/live#camera-state) for the difference between turning a camera off and disabling it.
+Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.

 ### `frigate/<camera_name>/enabled/state`

-Topic with current runtime state of processing for a camera. Published values are `ON` and `OFF`.
+Topic with current state of processing for a camera. Published values are `ON` and `OFF`.

 ### `frigate/<camera_name>/detect/set`

-Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
+Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`.

 ### `frigate/<camera_name>/detect/state`

@@ -384,7 +384,7 @@ Topic with current state of object detection for a camera. Published values are

 ### `frigate/<camera_name>/audio/set`

-Topic to turn audio detection for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
+Topic to turn audio detection for a camera on and off. Expected values are `ON` and `OFF`.

 ### `frigate/<camera_name>/audio/state`

@@ -392,7 +392,7 @@ Topic with current state of audio detection for a camera. Published values are `

 ### `frigate/<camera_name>/recordings/set`

-Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
+Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`.

 ### `frigate/<camera_name>/recordings/state`

@@ -400,7 +400,7 @@ Topic with current state of recordings for a camera. Published values are `ON` a

 ### `frigate/<camera_name>/snapshots/set`

-Topic to turn snapshots for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
+Topic to turn snapshots for a camera on and off. Expected values are `ON` and `OFF`.

 ### `frigate/<camera_name>/snapshots/state`

@@ -23,7 +23,7 @@ The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant das

 ## [Double Take](https://github.com/skrashevich/double-take)

-[Double Take](https://github.com/skrashevich/double-take) provides a unified UI and API for processing and training images for facial recognition.
+[Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
 It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
 This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.

@@ -31,10 +31,6 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht

 [Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.

-## [Frigate Notify Alert](https://github.com/Sysoev86/frigate-notify-alert)
-
-[Frigate Notify Alert](https://github.com/Sysoev86/frigate-notify-alert) sends Frigate events to Telegram as a photo + video media group. It supports multiple camera groups (each notifying its own chat), optional zone filtering (notify only when an object enters a chosen zone), and in-chat buttons to pause notifications for a set time. Works with standalone Frigate over MQTT; Home Assistant not required.
-
 ## [Frigate Snap-Sync](https://github.com/thequantumphysicist/frigate-snap-sync/)

 [Frigate Snap-Sync](https://github.com/thequantumphysicist/frigate-snap-sync/) is a program that works in tandem with Frigate. It responds to Frigate when a snapshot or a review is made (and more can be added), and uploads them to one or more remote server(s) of your choice.
@@ -53,7 +49,7 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht

 ## [Scrypted - Frigate bridge plugin](https://github.com/apocaliss92/scrypted-frigate-bridge)

-[Scrypted - Frigate bridge](https://github.com/apocaliss92/scrypted-frigate-bridge) is a plugin that allows you to ingest Frigate detections, motion, videoclips on Scrypted as well as provide templates to export rebroadcast configurations on Frigate.
+[Scrypted - Frigate bridge](https://github.com/apocaliss92/scrypted-frigate-bridge) is an plugin that allows to ingest Frigate detections, motion, videoclips on Scrypted as well as provide templates to export rebroadcast configurations on Frigate.

 ## [Strix](https://github.com/eduard256/Strix)

@@ -19,7 +19,7 @@ For the best results, follow these guidelines. You may also want to review the d

 ## AI suggested labels

-If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for your camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.
+If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for you camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.

 ![Suggestions](/img/plus/suggestions.webp)

@@ -3,10 +3,6 @@ id: first_model
 title: Requesting your first model
 ---

-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 ## Step 1: Upload and annotate your images

 Before requesting your first model, you will need to upload and verify at least 10 images to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
@@ -20,21 +16,13 @@ For more detailed recommendations, you can refer to the docs on [annotating](./a
 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.
 ![Plus Models Page](/img/plus/plus-models.jpg)

-## Step 3: Set your model
+## Step 3: Set your model id in the config

 You will receive an email notification when your Frigate+ model is ready.
 ![Model Ready Email](/img/plus/model-ready-email.jpg)

 Models available in Frigate+ can be used with a special model path. No other information needs to be configured because it fetches the remaining config from Frigate+ automatically.

-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Detectors and model" />. In the **Detection Model** section, choose the **Frigate+** tab. Select your new Frigate+ model from the **Available Frigate+ models** dropdown, then click **Save**. Restart Frigate to apply the change.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 detectors: ...

@@ -42,46 +30,22 @@ model:
  path: plus://<your_model_id>
 ```

-:::tip
-
-When setting the plus model id, all other fields should be removed as these are configured automatically with the Frigate+ model config
-
-:::
-
-</TabItem>
-</ConfigTabs>
-
 :::note

 Model IDs are not secret values and can be shared freely. Access to your model is protected by your API key.

 :::

+:::tip
+
+When setting the plus model id, all other fields should be removed as these are configured automatically with the Frigate+ model config
+
+:::
+
 ## Step 4: Adjust your object filters for higher scores

 Frigate+ models generally have much higher scores than the default model provided in Frigate. You will likely need to increase your `threshold` and `min_score` values. Here is an example of how these values can be refined, but you should expect these to evolve as your model improves. For more information about how `threshold` and `min_score` are related, see the docs on [object filters](../configuration/object_filters.md#object-scores).

-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.
-
-| Object            | Min Score | Threshold |
-| ----------------- | --------- | --------- |
-| **dog**           | .7        | .9        |
-| **cat**           | .65       | .8        |
-| **face**          | .7        |           |
-| **package**       | .65       | .9        |
-| **license_plate** | .6        |           |
-| **amazon**        | .75       |           |
-| **ups**           | .75       |           |
-| **fedex**         | .75       |           |
-| **person**        | .65       | .85       |
-| **car**           | .65       | .85       |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 objects:
  filters:
@@ -111,6 +75,3 @@ objects:
      min_score: .65
      threshold: .85
 ```
-
-</TabItem>
-</ConfigTabs>
@@ -24,7 +24,7 @@ Video decoding is one of the most CPU-intensive tasks in Frigate. While an AI ac

 ### Configuration

-Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/advanced/reference) as described in the [getting started guide](../guides/getting_started).
+Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/reference) as described in the [getting started guide](../guides/getting_started).

 ### Troubleshooting Hardware Acceleration

@@ -44,7 +44,7 @@ Choosing the right detector for your hardware is the single most important facto

 ### Understanding Detector Performance

-Frigate uses motion detection as a first-line check before running expensive object detection, as explained in the [motion detection documentation](../configuration/motion_detection). When motion is detected, Frigate creates a "region" (the green boxes in the [debug viewer](/usage/live#the-single-camera-view)) and sends it to the detector. The detector's inference speed determines how many detections per second your system can handle.
+Frigate uses motion detection as a first-line check before running expensive object detection, as explained in the [motion detection documentation](../configuration/motion_detection). When motion is detected, Frigate creates a "region" (the green boxes in the debug viewer) and sends it to the detector. The detector's inference speed determines how many detections per second your system can handle.

 **Calculating Detector Capacity:** Your detector has a finite capacity measured in detections per second. With an inference speed of 10ms, your detector can handle approximately 100 detections per second (1000ms / 10ms = 100).If your cameras collectively require more than this capacity, you'll experience delays, missed detections, or the system will fall behind.

@@ -58,6 +58,7 @@ When a single detector cannot keep up with your camera count, some detector type

 For detailed instructions on configuring multiple detectors, see the [Object Detectors documentation](../configuration/object_detectors).

+
 **When to add a second detector:**

 - Skipped FPS is consistently > 0 even during normal activity
@@ -69,6 +70,4 @@ The model you use significantly impacts detector performance. Frigate provides d
 **Model Size Trade-offs:**

 - Smaller models (320x320): Faster inference, Frigate is specifically optimized for a 320x320 size model.
- Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.
-
-For more detail on picking the right size, see [Choosing a model size](../configuration/object_detectors.md#choosing-a-model-size).
+- Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.
@@ -3,8 +3,6 @@ id: dummy-camera
 title: Analyzing Object Detection
 ---

-import NavPath from "@site/src/components/NavPath";
-
 Frigate provides several tools for investigating object detection and tracking behavior: reviewing recorded detections through the UI, using the built-in Debug Replay feature, and manually setting up a dummy camera for advanced scenarios.

 ## Reviewing Detections in the UI
@@ -53,31 +51,17 @@ Only one replay session can be active at a time. If a session is already running

 :::

-### Starting Debug Replay
-
-Debug Replay can be started from several places in the UI. The starting point determines the time range that gets replayed.
-
- **History: Actions menu.** Navigate to <NavPath path="History > {camera}" />, open the **Actions** menu in the toolbar, and choose **Debug Replay**. From here you can pick a preset (**Last 1 Minute**, **Last 5 Minutes**), select a range directly on the timeline with **From Timeline**, or enter exact start and end times with **Custom**. This is the most flexible option and the best choice when you want to add padding around a detection. On mobile, the same options appear in the Actions drawer.
- **History: Detail Stream event menu.** While viewing a review item in the Detail Stream, open the menu on a tracked object's event card and choose **Debug Replay**. The replay range is set automatically to that object's start and end times.
- **Explore: search result menu.** From an Explore card, open the kebab menu and choose **Debug Replay**. The range is taken from the tracked object's lifecycle.
- **Explore: Tracking Details Actions menu.** Open a tracked object's **Tracking Details** dialog, then choose **Debug Replay** from the Actions menu. Same automatic range as the search result menu.
- **Exports: export card menu.** From <NavPath path="Exports" />, open the menu on an export and choose **Debug Replay** to loop the exported clip through the detection pipeline for the camera it was exported from.
-
-The Detail Stream, Explore, and Exports entry points use the underlying recording or export's bounds with a small amount of padding. This can be convenient for quick checks, but if a detection is short or you want extra "settle" time for motion and the detector, start the replay from the History Actions menu instead and widen the range manually.
-
 ### Variables to consider

 - The replay will not always produce identical results to the original run. Different frames may be selected on replay, which can change detections and tracking.
 - Motion detection depends on the exact frames used; small frame shifts can change motion regions and therefore what gets passed to the detector.
 - Object detection is not fully deterministic: models and post-processing can yield slightly different results across runs.
- In cases where a detection is short and a replay may only be a small number of frames, it is recommended to manually add some padding before and after the detection so that the motion and object detectors have time to settle into the scene. Rather than starting Debug Replay from Explore, navigate to History for your camera, choose Debug Replay from the Actions menu, and click the "From Timeline" or "Custom" option.
- The replay camera inherits the source camera's zones. Any automations that trigger on those zone names will fire for the replay camera as well. This can be helpful when debugging zone behavior, but may be unexpected. You can add a condition on the source camera's name in your automation if you want to exclude replay triggers.

 Treat the replay as a close approximation rather than an exact reproduction. Run multiple loops and examine the debug overlays and logs to understand the behavior.

 ## Manual Dummy Camera

-For advanced scenarios (such as testing with a clip from a different source, debugging ffmpeg behavior, or running a clip through a completely custom configuration), you can set up a dummy camera manually.
+For advanced scenarios — such as testing with a clip from a different source, debugging ffmpeg behavior, or running a clip through a completely custom configuration — you can set up a dummy camera manually.

 ### Example config

@@ -109,7 +93,7 @@ cameras:
 2. Add the temporary camera to `config/config.yml` (example above). Use a unique name such as `test` or `replay_camera` so it's easy to remove later.
   - If you're debugging a specific camera, copy the settings from that camera (frame rate, model/enrichment settings, zones, etc.) into the temporary camera so the replay closely matches the original environment. Leave `record` and `snapshots` disabled unless you are specifically debugging recording or snapshot behavior.
 3. Restart Frigate.
-4. Observe the [Debug view](/usage/live#the-single-camera-view) in the UI and logs as the clip is replayed. Watch detections, zones, or any feature you're looking to debug, and note any errors in the logs to reproduce the issue.
+4. Observe the Debug view in the UI and logs as the clip is replayed. Watch detections, zones, or any feature you're looking to debug, and note any errors in the logs to reproduce the issue.
 5. Iterate on camera or enrichment settings (model, fps, zones, filters) and re-check the replay until the behavior is resolved.
 6. Remove the temporary camera from your config after debugging to avoid spurious telemetry or recordings.

@@ -12,7 +12,7 @@ There are many possible causes for a USB coral not being detected and some are O

 :::tip

-Using `lsusb` or checking the hardware page in HA OS will show as `1a6e:089a Global Unichip Corp.` until Frigate runs an inference using the coral. So don't worry about the identification until after Frigate has attempted to detect the coral.
+Using `lsusb` or checking the hardware page in HA OS will show as `1a6e:089a Global Unichip Corp.` until Frigate runs an inferance using the coral. So don't worry about the identification until after Frigate has attempted to detect the coral.

 :::

@@ -43,13 +43,13 @@ Some users have reported that this older device runs an older kernel causing iss
 3. Start the docker container with Coral TPU enabled in the config
 4. The TPU would be detected but a few moments later it would disconnect.
 5. While leaving the TPU device plugged in, restart the NAS using the reboot command in the UI. Do NOT unplug the NAS/power it off etc.
-6. Open the control panel - info screen. The coral TPU will now be recognized as a USB Device - google inc
+6. Open the control panel - info scree. The coral TPU will now be recognised as a USB Device - google inc
 7. Start the frigate container. Everything should work now!

 ### QNAP NAS

-QNAP NAS devices, such as the TS-253A, may use connected Coral TPU devices if [QuMagie](https://www.qnap.com/en/software/qumagie) is installed along with its QNAP AI Core extension. If any of the features (`facial recognition`, `object recognition`, or `similar photo recognition`) are enabled, Container Station applications such as `Frigate` or `CodeProject.AI Server` will be unable to initialize the TPU device in use.
-To allow the Coral TPU device to be discovered, you must either:
+QNAP NAS devices, such as the TS-253A, may use connected Coral TPU devices if [QuMagie](https://www.qnap.com/en/software/qumagie) is installed along with its QNAP AI Core extension. If any of the features—`facial recognition`, `object recognition`, or `similar photo recognition`—are enabled, Container Station applications such as `Frigate` or `CodeProject.AI Server` will be unable to initialize the TPU device in use.
+To allow the Coral TPU device to be discovered, the you must either:

 1. [Disable the AI recognition features in QuMagie](https://docs.qnap.com/application/qumagie/2.x/en-us/configuring-qnap-ai-core-settings-FB13CE03.html),
 2. Remove the QNAP AI Core extension or
@@ -76,7 +76,7 @@ This is an issue due to outdated gasket driver when being used with new linux ke

 ### Not detected on Raspberry Pi5

-A kernel update to the RPi5 means an update to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
+A kernel update to the RPi5 means an upate to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)

 Specifically, add the following to config.txt

@@ -87,7 +87,7 @@ dtoverlay=pcie-32bit-dma-pi5

 ## Only One PCIe Coral Is Detected With Coral Dual EdgeTPU

-Coral Dual EdgeTPU is one card with two identical TPU cores. Each core has its own PCIe interface and motherboard needs to have two PCIe busses on the m.2 slot to make them both work.
+Coral Dual EdgeTPU is one card with two identical TPU cores. Each core has it's own PCIe interface and motherboard needs to have two PCIe busses on the m.2 slot to make them both work.

 E-key slot implemented to full m.2 electromechanical specification has two PCIe busses. Most motherboard manufacturers implement only one PCIe bus in m.2 E-key connector (this is why only one TPU is working). Some SBCs can have only USB bus on m.2 connector, ie none of TPUs will work.

@@ -29,7 +29,7 @@ You can open `chrome://media-internals/` in another tab and then try to playback

 ### What do I do if my cameras sub stream is not good enough?

-Frigate generally [recommends cameras with configurable sub streams](/frigate/hardware.md). However, if your camera does not have a sub stream that is a suitable resolution, the main stream can be resized.
+Frigate generally [recommends cameras with configurable sub streams](/frigate/hardware.md). However, if your camera does not have a sub stream that a suitable resolution, the main stream can be resized.

 To do this efficiently the following setup is required:

@@ -49,13 +49,13 @@ This almost always means that the width/height defined for your camera are not c

 These messages in the logs are expected in certain situations. Frigate checks the integrity of the recordings before storing. Occasionally these cached files will be invalid and cleaned up automatically.

-### "MQTT connected" repeats in the logs
+### "On connect called"

-If you see repeated "MQTT connected" messages in your logs, check for another instance of Frigate. This happens when multiple Frigate containers are trying to connect to MQTT with the same `client_id`.
+If you see repeated "On connect called" messages in your logs, check for another instance of Frigate. This happens when multiple Frigate containers are trying to connect to MQTT with the same `client_id`.

 ### Error: Database Is Locked

-SQLite does not work well on a network share, if the `/media` folder is mapped to a network share then [this guide](../configuration/advanced/system.md#database) should be used to move the database to a location on the internal drive.
+SQLite does not work well on a network share, if the `/media` folder is mapped to a network share then [this guide](../configuration/advanced.md#database) should be used to move the database to a location on the internal drive.

 ### Unable to publish to MQTT: client is not connected

@@ -124,32 +124,3 @@ cameras:
      width: 1280
      height: 720
 ```
-
-### Why does Frigate keep creating new tracked objects for my parked car?
-
-Stationary tracking is designed to _prevent_ this: a parked car should remain a single tracked object rather than generating new ones. If you're repeatedly getting new tracked objects for the same car, it's likely that Frigate is losing the object and re-detecting it as a new one.
-
-Open one of the tracked objects in Explore → **Tracking Details**. If the detection scores are low (< 70% or so), the model isn't confident the parked car is a car. This is common with the free [COCO-trained](https://cocodataset.org/#explore) object detection models on steep/top-down angles, partially occluded cars, foliage, or low-light footage. When detections fall below `min_score` for too many frames the tracker loses the object, and the next confident frame creates a brand new one.
-
-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.
- **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.
-
-See [Object Filters](../configuration/object_filters.md) for more on tuning `min_score` and `threshold`. Note that raising them too high will make this exact problem worse.
-
-### How do I correct Frigate when it detects something as the wrong object?
-
-Frigate's object detection relies on a machine learning [model](../frigate/glossary.md#model), and the free [COCO-trained](https://cocodataset.org/#explore) models that ship with Frigate can misidentify objects in scenes they weren't trained on. There are two ways to handle this, depending on whether you want to _teach_ the model or just _suppress_ the bad result.
-
-**Train or fine-tune a model with your own images.** The most durable fix is to improve the model itself. The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. Some tools are freely available, and [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. When Frigate mislabels something, open the tracked object in Explore, select the **Snapshot** tab, and use **Submit to Frigate+** to send the example with the correct label (or mark it as a [false positive](../frigate/glossary.md#false-positive)). Once you've submitted examples and [requested a model](../plus/first_model.md), the retrained model will be more accurate for your cameras. See [Submitting examples to Frigate+](../integrations/plus.md#submit-examples) for the full workflow.
-
-**Suppress the misidentification with filters.** You can use filters to stop a specific false positive from being tracked:
-
- Tune `min_score` / `threshold`, or add `min_area` / `max_area` / `min_ratio` / `max_ratio` filters. See [Object Filters](../configuration/object_filters.md).
- If the false positive is always in the same fixed spot (like a statue or mailbox that reads as a person), add an [object filter mask](../configuration/masks.md#object-filter-masks) over that location.
-
-Filters and masks only hide the incorrect result - they don't teach Frigate what the object actually is. For that, fine-tune your own model or use Frigate+.
@@ -1,235 +0,0 @@
---
-id: go2rtc
-title: Troubleshooting go2rtc
---
-
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
-This page covers common problems with the bundled [go2rtc](/configuration/go2rtc) and how to resolve them, whether your cameras were added with the setup wizard or configured by hand.
-
-When a stream won't play or behaves oddly, the most important first step is to figure out **where** in the pipeline it breaks. Frigate's live view is a chain (_camera → go2rtc → your browser_), and each stage fails for different reasons. Work through the checks below in order, then jump to the matching problem category.
-
-## Start by isolating the problem
-
-### 1. Read the go2rtc logs
-
-Access the go2rtc logs in the Frigate UI under <NavPath path="System Logs" /> in the sidebar (select the **go2rtc** tab). If go2rtc cannot connect to your camera you will usually see a clear error here: `401 Unauthorized` (bad or incorrectly encoded credentials), `Connection refused` / `timeout` (wrong IP, port, or the camera is at its connection limit), or `404 Not Found` (wrong RTSP path, or the referenced stream name does not exist).
-
-### 2. Test the stream in the go2rtc web interface
-
-If the logs look clean, open go2rtc's own web interface on port `1984`. This is the single most useful diagnostic, because it takes Frigate's UI out of the equation entirely.
-
- If using Frigate through Home Assistant, enable the web interface at port `1984` (it is disabled by default, see [Home Assistant ports](#home-assistant-and-port-access)).
- If using Docker, forward port `1984` before accessing the web interface.
-
-Open the stream page for your camera (`http://<frigate_host>:1984/stream.html?src=back`) and try each player link:
-
- **If nothing plays here**, the problem is between the camera and go2rtc (codec, credentials, or transport), _not_ your browser. Fix it at the source before touching anything in Frigate.
- **If a player works here but Frigate's live view does not**, the problem is browser/codec related. Compare the **MSE** and **WebRTC** links. Frigate prefers MSE and only attempts WebRTC when MSE fails (or for two-way talk). If `mode=mse` plays but `mode=webrtc` does not, you have a [WebRTC codec problem](#webrtc-and-two-way-talk); if neither plays, your browser cannot decode the codec (commonly H.265, see [H.265 / HEVC cameras](#h265--hevc-cameras)).
-
-### 3. Inspect the negotiated codecs
-
-You can view detailed stream info, including the exact video and audio codecs go2rtc negotiated with the camera, at `http://frigate_ip:5000/api/go2rtc/streams` (or `http://frigate_ip:5000/api/go2rtc/streams/back` for a single camera). This is the authoritative answer to "what is my camera actually sending?" and is far more reliable than guessing from the camera's web UI. It also shows whether the audio track is `sendonly`/`recvonly`, which matters for [two-way talk](#webrtc-and-two-way-talk).
-
-### 4. Fix the codec with the FFmpeg module
-
-If the camera plays in go2rtc but not in your browser, the video or audio codec is unsupported. Browsers can reliably play **H.264** video and **AAC** audio; many cannot play H.265/HEVC, and some camera audio (G.711/PCM, MJPEG containers, etc.) is not playable at all. The fix is to have go2rtc re-encode the stream on demand using its FFmpeg module.
-
-In the Frigate UI this is the **Use compatibility mode (ffmpeg)** toggle on a stream source; in YAML it is the `ffmpeg:` prefix on the source URL.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and expand your camera's stream.
-2. On the source you want to convert, click the **Use compatibility mode (ffmpeg)** button (the sliders icon next to the URL). This routes the source through go2rtc's FFmpeg module and reveals the transcoding options.
-3. Set **Video** to **Transcode to H.264** if your browser can't play the camera's video codec (e.g. H.265). Leave it on **Copy** to pass the video through untouched. This is much cheaper and should be your default whenever only the audio needs converting.
-4. Set **Audio** to **Transcode to AAC** (for MSE) or **Transcode to Opus** (for WebRTC) if the camera's audio codec is unsupported. Leave it on **Copy** to keep the original, or **Exclude** to drop audio entirely.
-5. When transcoding **video**, set **Hardware acceleration** to **Automatic (recommended)** so the encode runs on your GPU instead of the CPU. See [hardware-accelerated transcoding](#hardware-accelerated-transcoding-with-ffmpeg-8) for an important FFmpeg 8 caveat.
-6. **Save** the section, then reload the live view.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-go2rtc:
-  streams:
-    back:
-      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-      # transcode video to H.264 on the GPU; only needed if the browser can't play the source codec
-      - "ffmpeg:back#video=h264#hardware"
-```
-
-To convert audio only (leaving video untouched), or to convert both:
-
-```yaml
-go2rtc:
-  streams:
-    back:
-      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-      - "ffmpeg:back#audio=aac" # audio only, preferred when the video already plays
-      # or, to convert both video and audio:
-      # - "ffmpeg:back#video=h264#audio=aac#hardware"
-```
-
-</TabItem>
-</ConfigTabs>
-
-:::warning
-
-The `#`-modifiers (`#video=`, `#audio=`, `#hardware`, `#backchannel=0`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
-
-:::
-
-Transcoding video is resource intensive. Always prefer `#video=copy` (the **Copy** option) and only convert the track that is actually unsupported. If you must transcode video and have no hardware encoder available, the built-in jsmpeg view may be the better option.
-
-## Live view is black, buffering, or stuck in "low-bandwidth mode"
-
-When the live view shows a black screen, spins forever, or repeatedly drops to the lower-quality jsmpeg player ("low-bandwidth mode"), the stream almost always contains something the browser cannot decode over MSE, usually H.265 video or a non-AAC audio track. Confirm this in the go2rtc web UI (port `1984`): if MSE won't play there, Frigate can't play it either, since it uses the same pipeline.
-
-The fix is to produce an **H.264 + AAC** stream, either by changing your camera's firmware codecs or by transcoding in go2rtc (see [Fix the codec with the FFmpeg module](#4-fix-the-codec-with-the-ffmpeg-module)). A few other things worth checking:
-
- **Set the camera's I-frame (keyframe) interval to match its frame rate** (or "1x" on Reolink), and avoid "smart"/"+" codecs like _H.264+_ or _H.265+_. A long keyframe interval delays the first decodable frame past Frigate's startup timeout, which forces the fallback to jsmpeg. See [camera settings recommendations](/configuration/live#camera-settings-recommendations).
- **A spinner that never clears, even though video plays in VLC**, is often an unplayable _audio_ track stalling playback. Drop or transcode the audio (see below).
- **Remote/VPN viewing that buffers** while the LAN is fine is usually latency/jitter exceeding MSE's startup buffer. Set up [WebRTC](/configuration/live#webrtc-extra-configuration), which drops late frames instead of buffering.
-
-The general live-view behavior (smart streaming, the MSE → WebRTC → jsmpeg fallback chain, and how to read browser console errors) is documented in detail in the [Live view FAQ](/configuration/live#live-view-faq).
-
-## H.265 / HEVC cameras
-
-H.265/HEVC playback in the browser is unreliable and version-dependent. WebRTC does not support H.265 on some browsers, and MSE/HEVC support varies by browser, OS, and whether a hardware decoder is present. An H.265 stream that plays fine in VLC, the go2rtc web UI, and Frigate's recordings can still be blank in a live view.
-
-For dependable live viewing, use **H.264** for the stream the live view consumes:
-
- Point the live view at the camera's H.264 **substream** and keep the H.265 main stream for recording only, or
- Transcode H.265 → H.264 in go2rtc with the FFmpeg module and `#hardware` (software HEVC transcoding is very CPU heavy).
-
-Treat browser HEVC playback as best-effort. See also [H.265 cameras via Safari](/configuration/camera_specific#h265-cameras-via-safari).
-
-## No audio in Live view
-
-Live view audio has strict codec requirements that differ by player: **MSE requires AAC, PCMA, or PCMU**, and **WebRTC requires Opus, PCMA, or PCMU**. Many cameras default to a codec outside these sets (or to PCM/G.711), so the player loads video only and no audio control appears.
-
-The most robust approach is to provide both an AAC track (for MSE) and an Opus track (for WebRTC) on the same stream by transcoding audio with the FFmpeg module while copying the video:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > System > go2rtc Streams" /> and expand the camera's stream.
-2. Add a second **Source** that references the stream by name (e.g. the URL `ffmpeg:back`), enable **Use compatibility mode (ffmpeg)**, and set **Audio** to **Transcode to Opus** for WebRTC support.
-3. Keep the original source as **Source 1** so MSE can use the camera's AAC (or transcode the first source's audio to AAC if the camera doesn't provide it).
-4. **Save** the section.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-go2rtc:
-  streams:
-    back:
-      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2 # video + AAC for MSE
-      - "ffmpeg:back#audio=opus" # adds an Opus track for WebRTC
-```
-
-If the camera's native audio isn't AAC either, transcode both:
-
-```yaml
-go2rtc:
-  streams:
-    back:
-      - "ffmpeg:rtsp://user:password@10.0.10.10:554/live0#video=copy#audio=aac" # video copy + AAC for MSE
-      - "ffmpeg:back#audio=opus" # Opus for WebRTC
-```
-
-</TabItem>
-</ConfigTabs>
-
-Setting the camera firmware to AAC (and H.264) avoids transcoding entirely and is always preferable when the camera supports it. For more detail and examples, see [Audio Support](/configuration/live#audio-support).
-
-## WebRTC and two-way talk
-
-WebRTC is only attempted when MSE fails or when using a camera's two-way talk feature; the "All Cameras" dashboard never uses it. When it doesn't work, the cause is almost always one of:
-
- **Codec mismatch**: WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
- **Port `8555` not reachable, or no candidates set**: WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
-
-## High CPU usage
-
-If go2rtc is using a lot of CPU, it is almost always transcoding in software. An FFmpeg source with a codec modifier like `#video=h264` or `#audio=aac` but **no** `#hardware` re-encodes on the CPU. (Frigate's `ffmpeg.hwaccel_args` only applies to Frigate's own detect/record processes. It does _not_ accelerate go2rtc's transcodes.)
-
-To keep CPU usage down:
-
- Only transcode the track that is genuinely unsupported, and use `#video=copy` to pass video through untouched whenever possible.
- When you must transcode video, always add `#hardware` (the **Automatic** hardware option in the UI) so the encode runs on the GPU. Note the [FFmpeg 8 device requirement](#hardware-accelerated-transcoding-with-ffmpeg-8) below.
- Don't restream a high-resolution main stream just to feed the live view: even with `#video=copy`, muxing a 4K/8MP+ stream is inherently expensive. Use the camera's lower-resolution substream for live and detect, and let Frigate pull the main stream directly for recording.
-
-## Connection, authentication, and complex passwords
-
-If go2rtc logs `401 Unauthorized` for a URL that works in VLC, the password almost certainly contains reserved URL characters. **Frigate URL-encodes passwords for its own `cameras.ffmpeg.inputs`, but it does not touch what you write under `go2rtc.streams`**: go2rtc parses that URL itself. You must URL-encode special characters yourself in the `go2rtc.streams` section (`@` → `%40`, `#` → `%23`, `?` → `%3F`, `%` → `%25`, etc.).
-
-Note the asymmetry: under `cameras.ffmpeg.inputs` you should use the **raw** password (Frigate encodes it for you). Pre-encoding it there causes a double-encode and fails. See [Handling Complex Passwords](/configuration/restream#handling-complex-passwords).
-
-Repeated `401`/`Connection refused` errors can also mean the camera hit its **concurrent connection limit** or triggered a login lockout. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) means the camera only ever sees one connection from go2rtc.
-
-## Stream names must match everywhere
-
-A surprising number of "the better live options aren't available" or `404 Not Found` problems come down to a name mismatch. The same string must be used consistently:
-
- the **go2rtc stream key** (`go2rtc.streams.<name>`),
- any `ffmpeg:<name>#…` source that references it,
- the camera's restream input path (`rtsp://127.0.0.1:8554/<name>`), and
- the camera name itself (so Frigate auto-maps it for MSE/WebRTC), or an explicit `live -> streams` mapping pointing at the go2rtc stream **name** (never a path).
-
-If you rename or remove a go2rtc stream while experimenting and the live stream selector then shows a blank entry, clear your browser's site data for the Frigate URL. The selected stream is cached per-device in local storage.
-
-## Camera-specific behavior
-
-Several camera brands have well-known quirks with go2rtc. Rather than repeat them here, see the [camera-specific configuration](/configuration/camera_specific) page, which covers them in detail. The highlights:
-
- **Reolink**: RTSP is unreliable on many models; the **http-flv** stream through the FFmpeg module is recommended, and you must enable HTTP/RTMP in the camera and **reboot** it. 6MP+ models stream H.265 over http-flv-enhanced, which requires FFmpeg 8.0. See [Reolink Cameras](/configuration/camera_specific#reolink-cameras).
- **TP-Link Tapo**: use go2rtc's native `tapo://` source for stability and two-way audio; a stale RTSP credential can often be revived by clicking play once in the go2rtc web UI.
- **Ubiquiti/UniFi Protect**: use the `rtspx://` scheme (not `rtsps://…?enableSrtp`).
- **Amcrest/Dahua**: use the `/cam/realmonitor?channel=1&subtype=N` scheme, where `subtype=0` is the main stream. See [Amcrest & Dahua](/configuration/camera_specific#amcrest--dahua).
-
-## Non-RTSP sources and the FFmpeg module
-
-go2rtc's native zero-copy handling only supports well-formed RTSP H.264/H.265. Anything else (MJPEG, HTTP/HTTP-FLV, RTMP, or unusual codecs) must be handed to the FFmpeg module by prefixing the source with `ffmpeg:`. This is also necessary for some camera streams to be parsed at all, at the cost of slightly slower startup. MJPEG and other non-H.264 sources additionally need `#video=h264` (with `#hardware`) before they can be used for the `record`, `detect`, or restream roles. See [MJPEG Cameras](/configuration/camera_specific#mjpeg-cameras) for a complete example.
-
-## Hardware-accelerated transcoding with FFmpeg 8
-
-Frigate 0.18 ships **FFmpeg 8.0** as the default, and FFmpeg 8 is stricter about hardware-accelerated filtering than earlier versions. Whenever go2rtc transcodes video with hardware acceleration (any source using `#hardware`, `#hardware=vaapi`, or the **Automatic** hardware option in the UI), it builds a filter chain that uploads frames to the GPU with the `hwupload` filter. FFmpeg 8 now refuses to do this unless it is told **which device** to use. Earlier versions selected one automatically. The result is that an otherwise-working transcode fails to start, the live view never loads, and go2rtc logs:
-
-```
-[hwupload] A hardware device reference is required to upload frames to.
-[AVFilterGraph] Error initializing filters
-Error opening output files: Invalid argument
-```
-
-The fix is to tell go2rtc's bundled FFmpeg which hardware device to use via the `go2rtc -> ffmpeg -> global` option. For **VAAPI**-based acceleration (which covers most Intel and AMD GPUs, and is what go2rtc selects automatically on that hardware), point it at your render device:
-
-```yaml
-go2rtc:
-  ffmpeg:
-    global: "-vaapi_device /dev/dri/renderD128"
-  streams:
-    back:
-      - "ffmpeg:rtsp://user:password@10.0.10.10:554/live0#video=h264#hardware"
-```
-
-`/dev/dri/renderD128` is the usual render node; on a system with more than one GPU you may need `renderD129` (or higher), and the device must be passed into the container (e.g. `devices: - /dev/dri:/dev/dri` in Docker Compose).
-
-If you use a **different hardware acceleration backend**, you will likely need to specify its device in the same way, using the option that matches that backend instead of `-vaapi_device`. See the [go2rtc FFmpeg source documentation](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-ffmpeg) and the upstream report ([go2rtc issue #1984](https://github.com/AlexxIT/go2rtc/issues/1984)) for background and other examples.
-
-:::tip
-
-If you don't transcode in go2rtc with hardware acceleration, this does not affect you. If you want to avoid the change entirely, you can pin Frigate (and the go2rtc it bundles) back to FFmpeg 7.0 by setting `ffmpeg -> path: "7.0"` in your config.
-
-:::
-
-## Home Assistant and port access
-
-When running Frigate as a Home Assistant App, the go2rtc API (port `1984`), the RTSP restream (port `8554`), and WebRTC (port `8555`) are **disabled and hidden by default**. To use them (for example to reach the go2rtc web interface for troubleshooting, or to open a go2rtc stream externally in an app like VLC), go to <NavPath path="Settings > Apps > Frigate > Configuration > Network" />, click **Show disabled ports**, enable the port you need, and save. Use the host's IP address rather than an mDNS name like `homeassistant.local`.
-
-If live view works in the Frigate UI but not in Home Assistant, the most common cause is the go2rtc stream name not matching the camera name: name the primary go2rtc stream exactly like the camera, or add a `live -> streams` mapping, so the integration can resolve the restream.
@@ -10,47 +10,4 @@ title: GPU Errors
 Some users have reported issues using some Intel iGPUs with OpenVINO, where the GPU would not be detected. This error can be caused by various problems, so it is important to ensure the configuration is setup correctly. Some solutions users have noted:

 - In some cases users have noted that an HDMI dummy plug was necessary to be plugged into the motherboard's HDMI port.
- When mixing an Intel iGPU with Nvidia GPU, the devices can be mixed up between `/dev/dri/renderD128` and `/dev/dri/renderD129` so it is important to confirm the correct device, or map the entire `/dev/dri` directory into the Frigate container.
-
-## Intel/AMD GPU
-
-### Hardware acceleration is not being used
-
-For VAAPI or QSV to work, the GPU's render device must be passed through to the Frigate container. Intel and AMD GPUs expose this as a render node under `/dev/dri`, usually `/dev/dri/renderD128`. If it is not passed through, hardware acceleration is unavailable: ffmpeg fails to initialize it (for example `Failed to open the drm device` or `No VA display found for device`) and GPU usage stays at zero while CPU usage remains high.
-
-Pass the render device through when starting the container. With `docker compose`:
-
-```yaml
-services:
-  frigate:
-    devices:
-      - /dev/dri/renderD128:/dev/dri/renderD128 # Intel / AMD GPU, update for your hardware
-```
-
-Or with `docker run`, add `--device /dev/dri/renderD128`. See the [installation docs](/frigate/installation) for a complete example.
-
-If it still isn't working after passing the device through:
-
- **Confirm the render node exists and is the correct one.** Run `ls /dev/dri` on the host. You should see one or more `renderD12X` entries. Systems with more than one GPU (an Intel iGPU plus a discrete GPU) can expose both `/dev/dri/renderD128` and `/dev/dri/renderD129`, and the numbering is not guaranteed. Pass through the correct node, or map the entire directory (`/dev/dri:/dev/dri`, or `--device /dev/dri`) so all render nodes are available.
- **Check device permissions.** The Frigate process must be able to access the render node. This is usually automatic when the container runs as root (the default), but nested setups such as an unprivileged Proxmox/LXC container often require making the device accessible on the host (for example, a world-readable render node) or running the container privileged. Note that running Frigate inside an LXC is not officially supported. See the [installation docs](/frigate/installation#proxmox) for details.
-
-### Failed to download frame: -5
-
-When using VAAPI or QSV hardware acceleration, ffmpeg may crash and restart periodically with a signature like this in the `ffmpeg.<camera>.detect` log:
-
-```
-[AVHWFramesContext @ 0x...] Failed to sync surface ... (operation failed).
-[hwdownload @ 0x...] Failed to download frame: -5.
-[vf#0:0 @ 0x...] Error while filtering: Input/output error
-[vf#0:0 @ 0x...] Task finished with error code: -5 (Input/output error)
-[frigate.video] <camera>: Unable to read frames from ffmpeg process.
-```
-
-This is a hardware frame synchronization failure between ffmpeg and the GPU driver, not a Frigate bug. It comes from how a specific camera stream interacts with the GPU's decode and scaling path, so it is highly dependent on your hardware, driver, and stream. Frigate's automatic hardware acceleration detection is a best-guess effort, so the fix is usually to tune the configuration for your specific hardware and camera. The solutions below are ordered from most to least likely to help:
-
- **Switch between the VAAPI and QSV presets.** On Intel Gen 12 and newer iGPUs, `preset-intel-qsv-h264` / `preset-intel-qsv-h265` is often more stable than the auto-detected `preset-vaapi`. See the [hardware acceleration docs](/configuration/hardware_acceleration_video.md#intel-based-cpus) for the recommended preset for your Intel generation.
- **Try a different VAAPI driver.** The default driver is `iHD`. On older Intel CPUs, `LIBVA_DRIVER_NAME=i965` can be more stable; on AMD GPUs use `LIBVA_DRIVER_NAME=radeonsi`. See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md#intel-based-cpus) for how to set the driver.
- **Use a codec that decodes more reliably.** H.265/HEVC streams may trigger this error far more often than H.264 depending on your CPU generation. If your camera exposes a separate sub-stream, assign an H.264 stream to the `detect` role. Cameras that output full-range YUV (for example some Hikvision models) are especially prone to it.
- **Match the detect resolution to the stream resolution.** When the `detect` resolution differs from the stream, Frigate inserts a GPU scaling filter (`scale_vaapi`), which is where these surface-sync failures can often originate. Set the `detect` `width` and `height` to match the exact resolution of the stream assigned the `detect` role.
- **Match the detect `fps` to the camera stream.** Aggressively dropping frames (for example `detect` `fps: 1` on a stream that runs at 15 fps) can cause timing mismatches in the GPU's frame buffer. Lower the sub-stream's frame rate on the camera itself instead of dropping most frames in Frigate.
- **Fall back to software decoding.** If none of the above resolve it, remove the preset for that camera (`hwaccel_args: []`). Hardware decoding is only an optimization. On a capable CPU, software-decoding a low-resolution sub-stream is inexpensive and gives a stable detect pipeline.
+- When mixing an Intel iGPU with Nvidia GPU, the devices can be mixed up between `/dev/dri/renderD128` and `/dev/dri/renderD129` so it is important to confirm the correct device, or map the entire `/dev/dri` directory into the Frigate container.
@@ -3,105 +3,6 @@ id: recordings
 title: Recordings Errors
 ---

-## Why are my recordings not working? (empty Recordings, "No recordings found for this time")
-
-If Frigate shows live video but the History view is empty, or you see "No recordings found for this time", the cause is almost always in one of the three categories below. Segments are first written to the RAM cache and are only moved to disk if they match a retention policy _and_ the camera's `record` stream is producing valid, storable video. Work through the categories in order: retention configuration is by far the most common cause.
-
-Before diving in, enable debug logging for the recording maintainer so you can see whether segments are being written to disk at all:
-
-```yaml
-logger:
-  logs:
-    frigate.record.maintainer: debug
-```
-
-A healthy camera logs lines like `Copied /media/frigate/recordings/{segment_path} in 0.2 seconds`. If you never see these, no segments are reaching disk, which points at the camera/stream or storage sections below.
-
-### Retention configuration issues
-
-#### Recording is enabled, but nothing is saved
-
-This is the single most common cause. Setting `record.enabled: True` on its own does **not** keep any footage: **continuous recording is disabled by default**, and segments in the cache are only moved to disk if they match a configured retention policy. You must configure at least one of `continuous`, `motion`, `alerts`, or `detections` retention.
-
-To store all video (the most conservative option), configure continuous retention:
-
-```yaml
-record:
-  enabled: True
-  continuous:
-    days: 3 # keep all footage for 3 days
-```
-
-See [Recording](/configuration/record) for the full set of common configurations, including reduced-storage and alerts-only setups.
-
-#### Motion or event-only recording keeps less than you expect
-
-If you only configured `motion`, `alerts`, or `detections` retention (with no `continuous`), Frigate keeps footage selectively based on the retention `mode`:
-
- **`mode: motion`** (the default) only retains segments that contain motion. If your [motion masks](/configuration/motion_detection) cover the areas where activity happens, or your motion sensitivity is too low, nothing will be retained even though recording is "on".
- **`mode: active_objects`** only retains segments where a tracked object was actively moving.
- **`mode: all`** retains every segment in the window.
-
-If you expected continuous footage but only configured motion/event retention, add a `continuous` retention period as shown above. To verify motion is actually being detected, watch the motion boxes in the debug view or the Motion Tuner in the UI.
-
-#### Alert and detection recordings require working object detection
-
-`alerts` and `detections` retention only keep footage that overlaps a tracked object, so they depend on object detection running:
-
- **Detection must be enabled.** If `detect: enabled: False`, no alerts or detections are ever created, so alert/detection retention keeps nothing. (Continuous and motion retention still work with detection disabled.)
- **The object must be supported by your model.** If you track an object your model doesn't support (for example `deer` or `license_plate` on the default model), Frigate never detects it and never records for it. Check your logs for warnings such as `... is configured to track ['deer'] objects, which are not supported by the current model` and remove unsupported objects or switch to a model (e.g. [Frigate+](/plus/)) that includes them.
-
-#### You're following an outdated guide
-
-Configuration keys change between major versions. The old `clips` config, for example, has not existed for a long time. If you copied a config from an old blog post or video, verify every key against the current [reference config](/configuration/advanced/reference).
-
-### Camera and stream issues
-
-#### Incompatible audio codec (recordings silently fail to save)
-
-Frigate stores recordings in an MP4 container, and some camera audio codecs (most commonly `pcm_alaw`, `pcm_mulaw`, or other G.711 variants) **cannot be placed in an MP4 container**. When this happens, ffmpeg fails to write the segment and no recording is saved, even though the live view works fine. This is a frequent cause on Tapo, TP-Link VIGI, and some Reolink cameras.
-
-Transcode the audio to AAC (or drop it entirely) using the appropriate [ffmpeg preset](/configuration/ffmpeg_presets):
-
-```yaml
-cameras:
-  your_camera:
-    ffmpeg:
-      output_args:
-        record: preset-record-generic-audio-aac # transcode audio to AAC
-        # or preset-record-generic to record with no audio
-```
-
-#### The record stream isn't connecting
-
-A message like `No new recording segments were created for <camera> in the last 120s` means ffmpeg cannot read the `record` stream. To diagnose:
-
- Confirm a stream is actually assigned the `record` role in your camera's `ffmpeg.inputs`.
- Open the go2rtc web interface on port `1984` and click each stream to confirm it plays. go2rtc errors such as `wrong response on DESCRIBE` or `start from CONN state` indicate the camera connection is failing.
- Test the exact RTSP URL (with the correct path, port, and credentials) in VLC or `ffplay`.
- If you restream through go2rtc, make sure the `record` input path points at the correct go2rtc stream name. Copying a config between cameras without updating the stream name is a common mistake.
-
-#### Recordings play back with no video (or won't play at all)
-
-Frigate copies the `record` stream directly without re-encoding, so playback depends on your browser supporting the camera's codec. H265/HEVC recordings may not be playable in some browsers. If recordings appear as audio-only or a black screen, your camera is likely sending a codec your browser can't decode. Configure the camera to output **H264** for maximum compatibility.
-
-#### Segments are only ~1 second long
-
-If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parameters mid-stream, corrupt timestamps cause segments to be split far too frequently and fill the cache. This produces the "Too many unprocessed recording segments" warning. See [that section below](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) for the full diagnosis.
-
-### Storage and mounting issues
-
-#### The storage volume isn't mounted correctly
-
-If the recordings volume (`/media/frigate`) points at the wrong location, isn't writable, or a network/encrypted mount failed to mount at boot, Frigate cannot save recordings, or it silently writes to the boot drive and then purges aggressively because the drive appears far smaller than expected.
-
- Compare the host's real capacity (`df -h`) against what the **Storage** page in the Frigate UI reports. A mismatch (for example Frigate reporting ~220 GB when your storage drive is 4 TB) means the bind mount is resolving to the wrong filesystem.
- Verify the host path in your Docker `volumes` mapping (`- /your/storage:/media/frigate`) exists and is writable by the container.
- For a mount that may fail intermittently, protecting the mount point with `chattr +i` on an empty directory forces Frigate to error out (rather than silently writing to the boot drive) when the mount is missing.
- Check `dmesg` and system logs for filesystem or I/O errors around the time recordings disappeared.
-
-If recordings _are_ being written but the copy is too slow to keep up, see the ["Unable to keep up with recording segments"](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) section below.
-
 ## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?

 You'll want to:
@@ -112,11 +13,7 @@ You'll want to:

 ## I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...

-This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.
-
-### Step 1: Enable recording debug logging
-
-The first step is to measure how long each segment takes to move from the RAM cache to disk. Enable debug logging for the recording maintainer:
+This error can be caused by a number of different issues. The first step in troubleshooting is to enable debug logging for recording. This will enable logging showing how long it takes for recordings to be moved from RAM cache to the disk.

 ```yaml
 logger:
@@ -124,34 +21,31 @@ logger:
    frigate.record.maintainer: debug
 ```

-This adds log lines showing the copy duration for each segment:
+This will include logs like:

 ```
 DEBUG   : Copied /media/frigate/recordings/{segment_path} in 0.2 seconds.
 ```

-Let this run until the warnings begin to appear, so you can confirm whether the disk is actually slowing down at the moment the error occurs.
+It is important to let this run until the errors begin to happen, to confirm that there is not a slow down in the disk at the time of the error.

-### Step 2: Interpret the copy times
+#### Copy Times > 1 second

-The copy duration tells you which direction to investigate:
+If the storage is too slow to keep up with the recordings then the maintainer will fall behind and purge the oldest recordings to ensure the cache does not fill up causing a crash. In this case it is important to diagnose why the copy times are slow.

- **Consistently longer than ~1 second**: your storage cannot keep up with the incoming recordings. Continue with Steps 3–5 to diagnose the slow storage.
- **Consistently well under 1 second**: storage is fast enough, and the problem is more likely CPU or resource contention. Skip to Step 6.
+##### Check RAM, swap, cache utilization, and disk utilization

-### Step 3: Check RAM, swap, cache, and disk utilization
+If CPU, RAM, disk throughput, or bus I/O is insufficient, nothing inside frigate will help. It is important to review each aspect of available system resources.

-If CPU, RAM, disk throughput, or bus I/O is insufficient, nothing inside Frigate will help. Review each aspect of available system resources while the warnings are occurring.
+On linux, some helpful tools/commands in diagnosing would be:

-On Linux, some helpful tools/commands for diagnosing this are:
+- docker stats
+- htop
+- iotop -o
+- iostat -sxy --human 1 1
+- vmstat 1

- `docker stats`
- `htop`
- `iotop -o`
- `iostat -sxy --human 1 1`
- `vmstat 1`
-
-On modern Linux kernels, the system will use some swap if it is enabled. Setting `vm.swappiness=1` no longer means the kernel will only swap in order to avoid OOM. To prevent any swapping inside the container, set the memory and memory+swap allocations to the same value and disable swapping by setting the following docker/podman run parameters:
+On modern linux kernels, the system will utilize some swap if enabled. Setting vm.swappiness=1 no longer means that the kernel will only swap in order to avoid OOM. To prevent any swapping inside a container, set allocations memory and memory+swap to be the same and disable swapping by setting the following docker/podman run parameters:

 **Docker Compose example**

@@ -173,23 +67,23 @@ services:
 --memory=<MAXRAM> --memory-swap=<MAXSWAP> --memory-swappiness=0
 ```

-NOTE: These are hard limits for the container, so be sure there is enough headroom above what `docker stats` shows for your container. It will immediately halt if it hits `<MAXRAM>`. In general, keeping all cache and tmp filespace in RAM is preferable to disk I/O where possible.
+NOTE: These are hard-limits for the container, be sure there is enough headroom above what is shown by `docker stats` for your container. It will immediately halt if it hits `<MAXRAM>`. In general, running all cache and tmp filespace in RAM is preferable to disk I/O where possible.

-### Step 4: Check your storage type
+##### Check Storage Type

-Mounting a network share is a popular option for storing recordings, but it can lead to reduced copy times and cause problems. Some users have found that using `NFS` instead of `SMB` considerably decreased copy times and fixed the issue. It is also important to ensure that the network connection between the device running Frigate and the network share is stable and fast. A saturated or unreliable link will stall copies.
+Mounting a network share is a popular option for storing Recordings, but this can lead to reduced copy times and cause problems. Some users have found that using `NFS` instead of `SMB` considerably decreased the copy times and fixed the issue. It is also important to ensure that the network connection between the device running Frigate and the network share is stable and fast.

-### Step 5: Check your mount options
+##### Check mount options

-Some users found that mounting a drive via `fstab` with the `sync` option dramatically reduced performance and led to this issue. Using `async` instead greatly reduced copy times.
+Some users found that mounting a drive via `fstab` with the `sync` option caused dramatically reduce performance and led to this issue. Using `async` instead greatly reduced copy times.

-### Step 6: Rule out CPU load
+#### Copy Times < 1 second

-If the copy times are consistently under 1 second but you still see the warning, the machine's CPU load is likely too high for Frigate to have the resources to keep up. Try temporarily shutting down other services, and any resource-intensive Frigate features, to see if the issue improves.
+If the storage is working quickly then this error may be caused by CPU load on the machine being too high for Frigate to have the resources to keep up. Try temporarily shutting down other services to see if the issue improves.

 ## I see the message: WARNING : Too many unprocessed recording segments in cache for camera. This likely indicates an issue with the detect stream...

-This warning means that the detect stream for the affected camera has fallen behind or stopped processing frames. Frigate's recording cache holds segments waiting to be analyzed by the detector. When more than 6 segments pile up without being processed, Frigate discards the oldest ones to prevent the cache from filling up.
+This warning means that the detect stream for the affected camera has fallen behind or stopped processing frames. Frigate's recording cache holds segments waiting to be analyzed by the detector — when more than 6 segments pile up without being processed, Frigate discards the oldest ones to prevent the cache from filling up.

 :::warning

@@ -199,7 +93,7 @@ This error is a **symptom**, not the root cause. The actual cause is always logg

 ### Step 1: Get the full logs

-Collect complete Frigate logs from startup through the first occurrence of the error. Look for errors or warnings that appear **before** the "Too many unprocessed" messages begin. That is where the root cause will be found.
+Collect complete Frigate logs from startup through the first occurrence of the error. Look for errors or warnings that appear **before** the "Too many unprocessed" messages begin — that is where the root cause will be found.

 ### Step 2: Check the cache directory

@@ -223,24 +117,18 @@ If segments are only ~1 second instead of ~10 seconds, the camera is sending cor

 **Common causes of short segments:**

- **"Smart Codec" or "Smart+" enabled on the camera**: These features dynamically change encoding parameters mid-stream, which corrupts timestamps. Disable them in your camera's settings.
- **Changing codec, bitrate, or resolution mid-stream**: Any encoding changes during an active stream can cause unpredictable segment splitting.
- **Camera firmware bugs**: Check for firmware updates from your camera manufacturer.
-
-:::tip
-
-You don't have to run `ffprobe` by hand to catch this. Open a camera's **Camera Probe Info** dialog (the info icon on the System → Metrics → Cameras page) and check the **Keyframe analysis** section. It probes the record stream and flags sparse or variable keyframes, which is what smart/"+" codecs (H.264+/H.265+) and long keyframe intervals produce.
-
-:::
+- **"Smart Codec" or "Smart+" enabled on the camera** — These features dynamically change encoding parameters mid-stream, which corrupts timestamps. Disable them in your camera's settings.
+- **Changing codec, bitrate, or resolution mid-stream** — Any encoding changes during an active stream can cause unpredictable segment splitting.
+- **Camera firmware bugs** — Check for firmware updates from your camera manufacturer.

 ### Step 4: Check for a stuck detector

 If the detect stream is not processing frames, segments will accumulate. Common causes:

- **Detection resolution too high**: Use a substream for detection, not the full resolution main stream.
- **Detection FPS too high**: 5 fps is the recommended maximum for detection.
- **Model too large**: Use smaller model variants (e.g., YOLO `s` or `t` size, not `e` or `x`). Use 320x320 input size rather than 640x640 unless you have a powerful dedicated detector.
- **Virtualization**: Running Frigate in a VM (especially Proxmox) can cause the detector to hang or stall. This is a known issue with GPU/TPU passthrough in virtualized environments and is not something Frigate can fix. Running Frigate in Docker on bare metal is recommended.
+- **Detection resolution too high** — Use a substream for detection, not the full resolution main stream.
+- **Detection FPS too high** — 5 fps is the recommended maximum for detection.
+- **Model too large** — Use smaller model variants (e.g., YOLO `s` or `t` size, not `e` or `x`). Use 320x320 input size rather than 640x640 unless you have a powerful dedicated detector.
+- **Virtualization** — Running Frigate in a VM (especially Proxmox) can cause the detector to hang or stall. This is a known issue with GPU/TPU passthrough in virtualized environments and is not something Frigate can fix. Running Frigate in Docker on bare metal is recommended.

 ### Step 5: Check for GPU hangs

@@ -258,7 +146,7 @@ An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume

 - After upgrading Frigate, verify your preset matches your hardware (e.g., `preset-intel-qsv-h264` instead of the deprecated `preset-vaapi`).
 - For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
- Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
+- Note that `hwaccel_args` are only relevant for the detect stream — Frigate does not decode the record stream.

 ### Step 7: Verify go2rtc stream configuration

@@ -268,46 +156,9 @@ Ensure that the ffmpeg source names in your go2rtc configuration match the corre

 If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:

- **CPU usage**: An overloaded CPU can prevent the detector from keeping up.
- **RAM and swap**: Excessive swapping dramatically slows all I/O operations.
- **Disk I/O**: Use `iotop` or `iostat` to check for saturation.
- **Storage space**: Verify you have free space on the Frigate storage volume (check the Storage page in the Frigate UI).
+- **CPU usage** — An overloaded CPU can prevent the detector from keeping up.
+- **RAM and swap** — Excessive swapping dramatically slows all I/O operations.
+- **Disk I/O** — Use `iotop` or `iostat` to check for saturation.
+- **Storage space** — Verify you have free space on the Frigate storage volume (check the Storage page in the Frigate UI).

 Try temporarily disabling resource-intensive features like `genai` and `face_recognition` to see if the issue resolves. This can help isolate whether the detector is being starved of resources.
-
-## I see the message: ERROR : Error occurred when attempting to maintain recording cache
-
-This message means the recording maintainer hit an error while moving segments from the cache to disk. It is a **generic wrapper**: the actual cause is always logged on the **very next line**. Frigate usually recovers and keeps running, but any affected segments are lost, so it is worth resolving.
-
-:::warning
-
-Always read the line immediately following this message. `Error occurred when attempting to maintain recording cache` on its own tells you nothing; the exception on the next line (for example `[Errno 28] No space left on device` or `[Errno 17] File exists`) is the real problem.
-
-:::
-
-Because these are operating-system-level errors, they must be resolved on the **host**, not within Frigate's configuration. The most common underlying errors are below.
-
-### [Errno 28] No space left on device
-
-The filesystem Frigate is writing to is full. Things to check:
-
- **The recordings volume is genuinely full.** Check free space on the host with `df -h` for the path mapped to `/media/frigate`, and review the **Storage** page in the Frigate UI.
- **The disk shows free space but is still "full".** This usually means the filesystem has run out of **inodes** (check with `df -i`), or recordings are landing on a different, smaller filesystem than you expect because of an incorrect bind mount. See [The storage volume isn't mounted correctly](#the-storage-volume-isnt-mounted-correctly) above.
- **`/tmp/cache` is full.** If you mounted `/tmp/cache` as a small `tmpfs`, a backlog of segments can fill it. Increase the tmpfs size, or address whatever is causing segments to pile up (see the [Too many unprocessed recording segments](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) section above).
- **The host blocks writes before Frigate can purge.** On some systems (for example Unraid with a fill-up threshold), the host stops writes before Frigate's emergency cleanup can run. Leave more headroom on the volume, or lower your retention so Frigate purges sooner.
-
-### [Errno 17] File exists (with ffmpeg "Error writing trailer" or "unable to re-open output file")
-
-Errors like `[Errno 17] File exists: '/media/frigate/recordings/.../<camera>'`, often alongside ffmpeg errors such as `Unable to re-open ... output file for shifting data` or `Error writing trailer: No such file or directory`, are a hallmark of an **unreliable network share** (NFS or SMB). The mount is dropping, serving stale directory entries, or mishandling file locking.
-
- Confirm the network connection to the NAS is stable and fast. An intermittent link produces these errors sporadically.
- Prefer **NFS over SMB** for the recordings mount; several users have found NFS more reliable and faster.
- Review your `fstab`/mount options for settings that hurt consistency or performance (see the `sync` vs `async` note in the [Unable to keep up with recording segments](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) section above).
- Enable `frigate.record.maintainer` debug logging to confirm whether the errors line up with the share becoming unavailable.
-
-### Errors referencing a camera you manually renamed or removed
-
-If the next-line error references a camera name that no longer exists in your config, orphaned data is left over from a rename or removal in a persistent `/tmp/cache` volume.
-
- Using a `tmpfs` mount for `/tmp/cache` as recommended in the [installation docs](/frigate/installation#storage) prevents stale cache files under the old camera name from surviving a restart, which avoids this issue entirely.
- If errors persist, stop Frigate and remove any leftover segments for the old camera name from `/tmp/cache`.
@@ -1,101 +0,0 @@
---
-id: explore
-title: Explore
---
-
-import NavPath from "@site/src/components/NavPath";
-
-**Explore** is where you browse and search every **tracked object** Frigate has saved. By default it groups recent objects by label; when [Semantic Search](/configuration/semantic_search) is enabled, you can also search by natural-language description or visual similarity. Selecting any object opens a detail pane with its snapshot, lifecycle, and metadata.
-
-This page describes how to _use_ the Explore view. For how the underlying features are _configured_, see [Semantic Search](/configuration/semantic_search) and [Generative AI descriptions](/configuration/genai/genai_objects).
-
-:::tip
-
-If you just want to quickly see what happened on your cameras, it's recommended to use [Review](/usage/review) rather than Explore. Review groups overlapping and adjacent activity on a camera into **review items** and sorts them into Alerts, Detections, and Motion, so you can scan and play back footage in a few clicks instead of sifting through individual objects. Reach for Explore when you need to find a _specific_ tracked object after the fact: by label, time, zone, or description.
-
-:::
-
-## Browsing tracked objects
-
-The default view shows your most recent tracked objects grouped into rows by label (_Person_, _Car_, _Dog_, and so on), each row labeled with the object type and a count. The arrow at the end of a row opens the full, filterable grid for that label.
-
-Clicking a thumbnail opens its [detail dialog](#tracked-object-details); right-clicking or long-pressing a thumbnail opens an [actions menu](#actions-and-bulk-selection). You can switch to a denser grid layout and adjust the number of columns from the view's settings.
-
-## Searching
-
-When [Semantic Search](/configuration/semantic_search) is enabled, a search bar appears that combines two things in one input:
-
- **Natural-language search**: type a free-text query and press Enter to run a semantic search over your tracked objects.
- **Filter tokens**: type a `key:` to get suggestions, then a value, to add a structured filter. Each filter becomes a removable chip, and you can chain several together.
-
-You can save a search with the star icon and reload it later, and clear everything with the clear-search icon. A help popover explains the token syntax, for example:
-
-```
-cameras:front_door label:person before:01012024 time_range:3:00PM-4:00PM
-```
-
-### Filter reference
-
-The most common filter tokens are:
-
-| Filter                       | Description                                                                        |
-| ---------------------------- | ---------------------------------------------------------------------------------- |
-| **Cameras**                  | Limit to one or more cameras.                                                      |
-| **Labels**                   | Object labels (person, car, etc.).                                                 |
-| **Sub Labels**               | Recognized sub labels (e.g. a recognized face or name).                            |
-| **Attributes**               | Classification attributes applied to the object.                                   |
-| **Recognized License Plate** | Match a recognized plate.                                                          |
-| **Zones**                    | Objects that entered specific zones.                                               |
-| **Before / After**           | Restrict to a date range.                                                          |
-| **Time Range**               | Restrict to a time of day (`HH:MM-HH:MM`).                                         |
-| **Min / Max Score**          | Restrict by the object's confidence score.                                         |
-| **Min / Max Speed**          | Restrict by estimated speed (when speed estimation is configured).                 |
-| **Has Snapshot / Has Clip**  | Only objects that saved a snapshot or recording.                                   |
-| **Submitted to Frigate+**    | Only objects already submitted (when Frigate+ is enabled).                         |
-| **Search Type**              | Whether semantic search matches the object's **Thumbnail** or its **Description**. |
-
-### Sorting
-
-When a filter or search is active, a **Sort** control lets you order results by **date**, **object score**, or **estimated speed** (ascending or descending). When a semantic query or similarity search is active, results can also be ordered by **relevance**.
-
-### Thumbnail and description search
-
- The **Search Type** setting controls whether a text query is matched against each object's **thumbnail** or its **description**. Each result indicates which one it matched and the confidence.
-
-Natural-language search, thumbnail search, and description search all require [Semantic Search](/configuration/semantic_search) to be enabled.
-
-## Tracked Object Details
-
-Selecting an object opens the **Tracked Object Details** dialog. Use the arrows (or the left/right keys) to step to the previous or next object. The dialog has two tabs:
-
- **Snapshot** or **Thumbnail**: the saved snapshot (or thumbnail).
- **Tracking Details**: the object's lifecycle, available when the object has a recording. It lists each significant moment (detected, entered a zone, became active or stationary, left, and so on); clicking a moment plays that part of the recording with the bounding box overlaid. A settings popover lets you show all zones and adjust the annotation offset.
-
-The details pane shows the object's **label**, **scores**, **camera**, **timestamp**, estimated **speed**, any **recognized license plate** and **classification attributes**, and its **description**. Admins can edit the sub label, license plate, and attributes inline.
-
-The **description** can be edited by hand, and, when [Generative AI descriptions](/configuration/genai/genai_objects) are enabled and the object's lifecycle has ended, regenerated from the snapshot or from thumbnails. For `speech` objects, a **Transcribe** action is available when audio transcription is enabled. When [Frigate+](/integrations/plus) is enabled, admins can submit a snapshot to improve their model directly from this pane.
-
-## Actions and bulk selection
-
-Right-clicking or long-pressing an object (in the grid or its thumbnail) opens an actions menu with options to **download** the video, snapshot, or a clean snapshot; **view tracking details**; **find similar**; **add a trigger**; **view in History**; and **delete the tracked object**.
-
-:::note
-
-Deleting a tracked object removes its snapshot, embeddings, and tracking-details entries, but the recorded footage of that object in [History](/usage/history) is **not** deleted.
-
-:::
-
-To act on many objects at once, Ctrl/Cmd-click or right-click to start a selection (selected tiles gain a blue ring), then use the toolbar to select all, clear the selection, or delete (admins).
-
-## Semantic Search - Usage and best practices {#usage-and-best-practices}
-
-1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
-2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
-3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the ones without them are more relevant.
-4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
-5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
-6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
-
-## Triggers
-
-From an object's actions menu, **Add trigger** sets up a per-camera trigger that uses Semantic Search to automate an action (a notification, sub label, or attribute) whenever a similar object appears. Triggers require Semantic Search and are managed under <NavPath path="Settings > Enrichments > Triggers" />. See [Triggers](/configuration/semantic_search#triggers) for full configuration and best practices.
@@ -1,43 +0,0 @@
---
-id: exports
-title: Exports
---
-
-**Exports** are how you keep a specific piece of footage permanently.
-
-Frigate's recordings are governed by your [retention settings](/configuration/record): once footage ages past its retention window (or, depending on your configuration, once it is only kept where motion, alerts, or detections occurred), it is deleted to free up disk space. An **export** saves a copy of a chosen time range to a separate location that is **never removed by retention**, so it stays available until you delete it yourself.
-
-This is the answer to the common question _"how do I stop Frigate from deleting an important clip?"_ Instead of increasing retention for an entire camera (which uses far more storage to protect a single moment), export just the footage you want to keep.
-
-:::tip
-
-Exports are stored under `/media/frigate/exports`, separate from your recordings, and are not counted against or removed by recording retention. They remain on disk until you delete them, so be aware that they accumulate over time.
-
-:::
-
-## Creating an export
-
-There are a few ways to create an export:
-
- **From Review**: select (right click or long-press) an individual review item directly, and choose Export from the header menu. You can also select multiple review items and export them all at once, optionally grouping them into a [case](#cases).
- **From History**: open the **Actions** menu and choose **Export**. You can export a preset duration (the last 1, 4, 8, 12, or 24 hours), enter a custom start and end time, or select a range directly on the timeline. A **multi-camera** option lets you export the same time range across several cameras at once.
-
-In every case you can give the export a name. Frigate then saves the footage from your recordings as a single video file. Larger ranges take time to process; the export is marked _in progress_ until it finishes, and you can keep using Frigate while it runs.
-
-## Managing exports
-
-All of your exports live on the **Exports** page, reachable from the main navigation, where you can search for one by name. Each export offers the following actions:
-
- **Play** it in the browser,
- **Download** it to save the footage outside of Frigate,
- **Share** it: copies a direct link to the export (or uses your device's share sheet),
- **Rename** it, and
- **Delete** it: deleting is the only way an export is removed.
-
-You can also select multiple exports at once to **delete** them in bulk, or to **add them to** (or **remove them from**) a [case](#cases).
-
-## Cases
-
-A **case** groups related exports together: for example, all the clips from a single incident across multiple cameras. On the **Exports** page you can create a case with a name and description, add existing exports to it (or create a new case while exporting), and **download the entire case as a single archive** to hand off as one package.
-
-Exports that don't belong to a case appear under **Uncategorized Exports**. Deleting a case lets you either keep its exports (they move back to uncategorized) or delete them along with the case.
@@ -1,75 +0,0 @@
---
-id: history
-title: History
---
-
-import NavPath from "@site/src/components/NavPath";
-
-**History** is Frigate's full-resolution recording viewer. Unlike Live, Review, and Explore, there is no menu item for it. You reach it from within another view, then scrub the timeline, switch cameras, inspect a tracked object's lifecycle, and export or share any moment.
-
-This page describes how to _use_ the History view. For how recordings are _configured_ (retention, pre/post capture), see [Recording](/configuration/record).
-
-## Opening History
-
-You can open History from several places:
-
- **From [Review](/usage/review):** clicking a review item opens its recording, scrubbed to just before the activity on that camera.
- **From [Live](/usage/live):** the **History** button in a camera's single-camera view opens that camera about 30 seconds in the past.
- **From a share link:** opening a shared timestamp link (see [Share Timestamp](#the-actions-menu) below) jumps straight to that camera and moment.
-
-Use the **Back** button to return where you came from, or the **Live** button to jump to the current camera's live view.
-
-:::tip
-
-If you see **"No recordings found for this time"**, the most common causes are: recording was not enabled for that camera at the time of the event; the retention window has since expired and those segments were removed; or storage ran low and Frigate deleted them early to free space. See [Recording](/configuration/record) to verify your retention settings.
-
-:::
-
-## Timeline, Events, and Detail
-
-A toggle (a drawer on mobile) switches the side panel between three modes:
-
- **Timeline**: a scrubbable vertical timeline of the selected camera. Horizontal lines down the center represent motion, with longer lines indicating more motion at that moment. Review items are marked as shaded areas (**red** for alerts, **orange** for detections), and sections with no colored background are times when no recording exists.
- **Events**: a scrollable list of the camera's review items for the time range; clicking one seeks the player to it.
- **Detail**: the [tracking details inspector](#the-detail-view) for the objects in view.
-
-While you are selecting a range to export, the panel temporarily switches to Timeline.
-
-## Scrubbing and previews
-
-Drag the timeline handlebar to move through time; the main player and any secondary camera previews scrub together so everything stays in sync. Press the zoom buttons on the timeline to change its zoom level (from coarse to fine segments). Sections of the timeline with no recordings are shown as gaps.
-
-On desktop, when more than one camera is available, a **row of secondary previews** shows the other cameras at the same moment. Clicking one of them makes it the main camera at the current timestamp, so you can follow activity across cameras without losing your place. On mobile, use the camera drawer to switch cameras.
-
-## Filtering and the calendar
-
-You can filter History by **cameras** and **date**. The calendar behaves the same as it does in [Review](/usage/review#filtering-and-the-calendar): an **underline** under a day means recordings exist for that day, and a **colored dot** (red for unreviewed alerts, orange for unreviewed detections) marks days with unreviewed activity.
-
-## The Detail view
-
-The **Detail** mode turns the side panel into a tracking details inspector. It lists one card per review item, each showing the item's severity, start time, the object labels involved, a count of tracked objects, and the duration. The active card is highlighted as the video plays, and clicking a card seeks to it.
-
-Expanding a card reveals the **lifecycle** of each tracked object: a row for each significant moment (detected, entered a zone, became active, became stationary, left, and so on), with a progress line that follows the current playback position. Hovering a row shows that moment's score, ratio, and area, and clicking a row seeks the video to that exact timestamp.
-
-The **Detail View Settings** at the bottom let you toggle whether the active item's objects expand automatically, and adjust the **annotation offset**: a fine timing correction that aligns the bounding-box overlays with the recorded video when your camera's snapshot and recording timestamps drift. Admins can save the offset to the camera's configuration.
-
-## The Actions menu
-
-On desktop, the **Actions** menu (the film icon) collects the things you can do with the footage you are viewing:
-
- **Export**: save a clip of a chosen time range so it is never removed by retention. The dialog pre-selects the last hour; adjust the range or drag the timeline handles, then export. See [Exports](/usage/exports) for managing and downloading exports.
- **Share Timestamp**: generate a link to the current moment (or a custom timestamp) to share with another Frigate user. This is an internal link, not a public share URL.
- **Motion Search**: scan this camera's recordings for changes in a region you draw. This is the same tool documented under [Reviewing Motion](/usage/review#motion-search).
- **Debug Replay** (admins): replay a recorded range back through Frigate's detection pipeline to see how it would be processed.
-
-You can also capture an instant snapshot of the current frame, and submit a frame to [Frigate+](/integrations/plus) directly from the player (admins only).
-
-## AI review summaries
-
-When [Generative AI review](/configuration/genai/genai_review) is configured, Frigate can generate a title, description, and threat classification for review items and surface them as you scrub through History. A review item that has an AI summary exposes its details in a few places:
-
- **Over the video**: when the item is on screen, a popup appears over the player.
- **In the Events side panel**: items with a summary show the title below the thumbnail.
- **In the Detail side panel**: the item's card shows the title alongside its tracking details.
-
-Clicking any of these opens the **AI Analysis** dialog with the generated detail and any flagged concerns for that item.
@@ -1,118 +0,0 @@
---
-id: live
-title: Live View
---
-
-import NavPath from "@site/src/components/NavPath";
-
-**Live view** is Frigate's real-time dashboard and the page you land on by default. It shows all of your cameras at a glance, streams your most recent alerts across the top, and lets you open any camera in a full-resolution single-camera view with audio, two-way talk, PTZ, and on-demand recording controls.
-
-This page describes how to _use_ the Live view. For how to _configure_ live streaming (go2rtc, stream selection, smart streaming, WebRTC, and audio), see the [Live View configuration](/configuration/live) docs.
-
-## The dashboard at a glance
-
-The default **All Cameras** dashboard shows every camera, with a filmstrip of recent **alerts** scrolling across the top. Clicking an alert opens it in [Review](/usage/review); each card also has a check button to mark it reviewed without leaving the dashboard. Only **alerts** appear in the filmstrip. To suppress a label or zone from showing there, configure it as a detection instead (see [Alerts and Detections](/configuration/review#alerts-and-detections)).
-
-By default Frigate uses **smart streaming**: a camera's image updates roughly once per minute while nothing is happening, and switches to a full live stream the moment activity is detected. This conserves bandwidth and resources. You can change this for each camera when using a camera group (see [Streaming settings](#streaming-settings-and-the-right-click-menu) below), and the behavior is explained in detail under [Live view technologies](/configuration/live#live-view-technologies).
-
-On mobile, a toggle in the header switches between a **grid** layout and a single-column **list** layout. On desktop a **fullscreen** button is available in the lower-right corner.
-
-## Switching dashboards and camera groups
-
-The icon rail (top-left on desktop, a horizontal strip on mobile) switches between dashboards:
-
- The **home** icon is the **All Cameras** dashboard, which shows every camera enabled for the dashboard.
- Each **camera group** you create appears as its own icon. Selecting a group shows only that group's cameras.
-
-Camera groups are useful for organizing cameras by location (for example, _Front of House_ or _Backyard_) and for giving each group its own dashboard layout and camera streaming preferences.
-
-You can also view [Birdseye](/configuration/birdseye) on the dashboard, or open it directly at `http://<frigate_host>:5000/#birdseye`. Clicking a camera inside the Birdseye view jumps to that camera's live feed.
-
-## Creating and editing camera groups
-
-Admins can manage groups from the pencil icon next to the group rail, which opens the **Camera Groups** dialog. From there you can add a group, or edit and delete existing ones. When creating a group you choose:
-
- a **Name** (spaces are converted to underscores),
- the **cameras** to include (each camera has a toggle and a gear that opens its [streaming settings](#streaming-settings-and-the-right-click-menu)), and
- an **icon** used for the group's button in the rail.
-
-Deleting a group also clears any custom layout you saved for it.
-
-## Rearranging a camera group layout
-
-On desktop and tablet, each camera group has its own freely-arrangeable grid. Enter **Edit Layout** mode from the layout button in the lower-right corner: camera tiles gain a drag handle and corner resize handles. Drag a tile to reposition it and drag a corner to resize it (the aspect ratio is preserved). Exit edit mode to save. The layout is stored in your browser per device, so each device can have its own arrangement.
-
-The default **All Cameras** dashboard is not manually arrangeable. It automatically sizes tiles based on each camera's aspect ratio (wide cameras span two columns, tall cameras span two rows).
-
-## Reading the tile indicators
-
-Each camera tile surfaces its current state with a few overlays:
-
- A **pulsing red dot** in the corner means **motion is currently detected** on that camera.
- A **red outline** around the tile means an **active tracked object** is on that camera.
- A small **label chip** lists the object types currently detected (for example, _Person_, _Car_).
- A **camera-name label** appears when you have enabled always-on camera names, or when a camera is offline or disabled.
- A **Stream Offline** or **Camera is off** placeholder appears when no frames are being received or the camera has been turned off.
-
-You can optionally overlay live streaming statistics (stream type, bandwidth, latency, and frame counts) on a tile to diagnose playback issues.
-
-## Streaming settings and the right-click menu
-
-Right-clicking (or long-pressing) a camera tile opens a context menu with quick controls: an **audio volume** control for streams that support audio, **Mute / Unmute all cameras**, **show or hide streaming statistics**, the **debug view**, **notification** options, and, for admins, turning the camera on or off. If the audio control doesn't appear, see [Audio Support](/configuration/live#audio-support). Audio requires go2rtc configured with a compatible codec.
-
-A **Low-bandwidth mode** notice may also appear in the context menu with a **Reset** option when Frigate has fallen back to the lower-quality jsmpeg stream. See the [Live view FAQ](/configuration/live#live-view-faq) for why this happens.
-
-For non-default groups, the context menu also exposes **Streaming Settings** for that camera, which let you choose:
-
- the **stream** to display (the dropdown lists the streams you configured under [`live -> streams`](/configuration/live#setting-streams-for-live-ui), and indicates whether audio is available),
- the **streaming method**: **No Streaming**, **Smart Streaming** (recommended), or **Continuous Streaming** (higher bandwidth), and
- **compatibility mode**, for devices that have trouble rendering the default player.
-
-These settings are saved per group and per device in your browser, not in your config file.
-
-## The single-camera view
-
-Clicking a camera tile opens its full-resolution single-camera view. The top bar provides:
-
- **Back** (also the `Esc` key) to return to the dashboard,
- **History** to jump to the [recordings](/usage/history) for this camera, starting about 30 seconds in the past,
- **Fullscreen** and **Picture-in-Picture** (if supported by your browser),
- **Two-way talk** (the microphone button, which requires a supported camera and WebRTC; keyboard shortcut `t`), and
- **Camera audio muting** (the speaker button; keyboard shortcut `m`).
-
-You can pinch or scroll to zoom into the feed. A **settings** gear provides a **stream** selector (with audio and two-way-talk availability indicators), **Play in background**, **Show stats**, and a **Debug view** that overlays Frigate's detection regions and bounding boxes.
-
-:::tip
-
-Two-way talk and camera audio have specific codec and port requirements. See [Audio Support](/configuration/live#audio-support) and [WebRTC](/configuration/live#webrtc-extra-configuration) for setup details.
-
-:::
-
-## Camera controls
-
-Admins get a row of toggles in the single-camera view (a settings drawer on mobile) to turn camera features on and off in real time:
-
- **Camera** on/off,
- **Object detection**,
- **Recording** (only available when recording is enabled in the camera's config),
- **Snapshots**,
- **Audio detection**,
- **Live audio transcription** (when audio detection is enabled), and
- **Autotracking** (for [autotracking-capable PTZ cameras](/configuration/autotracking)).
-
-These toggles change runtime behavior immediately. Whether a change persists across a restart depends on the feature. See the relevant configuration page.
-
-## On-demand recording and snapshots
-
-The single-camera view can capture footage on demand:
-
- **Start on-demand recording** begins a manual recording based on the camera's recording retention settings (the button pulses while active). If recording is disabled for the camera, only a snapshot is saved. Use **End on-demand recording** to stop.
- **Download instant snapshot** saves a still image of the current frame.
-
-See [Recording](/configuration/record) and [Snapshots](/configuration/snapshots) for how retention is configured, and [Exports](/usage/exports) for keeping a clip permanently.
-
-## PTZ controls
-
-For ONVIF cameras that support it, a control panel provides pan/tilt arrows, **zoom**, **focus**, and saved **presets**. You can also enable a **click-to-move / drag-to-zoom** overlay: click a point in the frame to center the camera there, or drag a box to pan and zoom to that area (dragging top-left to bottom-right zooms in, the reverse zooms out).
-
-For continuous, automatic tracking of a moving object, see [Autotracking](/configuration/autotracking).
@@ -1,140 +0,0 @@
---
-id: review
-title: Review
---
-
-import NavPath from "@site/src/components/NavPath";
-
-**Review** is where you triage what happened on your cameras. It groups activity into **review items**, segments of time on a single camera that bundle together the objects and audio that were active at once, and sorts them into **Alerts**, **Detections**, and **Motion**. From here you can scrub through activity, mark items as reviewed, filter, export, and jump to the full recording in [History](/usage/history).
-
-This page describes how to _use_ the Review view. For how alerts and detections are _configured_ (labels, zones, required zones, retention), see the [Review configuration](/configuration/review) docs.
-
-:::info
-
-Review items are only created for a camera when **object tracking and recording are enabled** for that camera. See [Recording](/configuration/record).
-
-:::
-
-## Alerts, Detections, and Motion
-
-Not every segment of video captured by Frigate is of the same level of interest. The people who enter your property may be a higher priority than those just walking by on the sidewalk. For this reason, Frigate sorts **review items** by importance into **alerts** and **detections**, with a separate **Motion** category for significant motion.
-
-The toggle at the top of the page switches between these three severities. One is always selected.
-
-| Tab            | Indicator color | What it shows                                                                                                    |
-| -------------- | --------------- | ---------------------------------------------------------------------------------------------------------------- |
-| **Alerts**     | dark red        | The activity you most want to see. By default, all `person` and `car` tracked objects are alerts.                |
-| **Detections** | orange          | Everything else Frigate tracked that wasn't promoted to an alert.                                                |
-| **Motion**     | yellow          | Periods of significant motion, with the ability to filter to periods which did **not** produce a tracked object. |
-
-This same color coding is used for the ring around a selected item and the dots on the calendar. How an object is categorized as an alert vs. a detection, and how required zones refine that, is covered in [Alerts and Detections](/configuration/review#alerts-and-detections).
-
-The **Alerts** and **Detections** tabs show a count next to their label. With **Show Reviewed** turned off (the default), this is the number of items still left to review; with it on, the count reflects every item in the selected time range.
-
-## Marking items as reviewed
-
-Review items are shown as a grid of thumbnail cards next to a vertical activity timeline. Hovering a card (desktop) or swiping to the right (mobile) plays a short preview inline.
-
- **Clicking** a card opens its recording in [History](/usage/history) and marks the item as reviewed.
- The object chip on each card is **gray** when the item is unreviewed and turns **green** once it has been reviewed.
- The **Mark these items as reviewed** button marks everything currently shown as reviewed at once.
-
-Reviewed state is tracked per user, so marking an item reviewed does not hide it for other users. Marking an item reviewed does not delete anything: the footage and the review item itself remain until they expire via retention.
-
-## Selecting and acting on multiple items
-
-To act on several items at once, start a selection by **Ctrl/Cmd-clicking** a card (desktop) or **long-pressing** one (mobile). Selected cards gain a colored ring matching their severity. Keyboard shortcuts speed this up: `Ctrl+A` selects all, `R` marks the selection reviewed, and `Esc` clears it.
-
-With items selected, an action bar appears with options to:
-
- **Export** the selected items (a single item exports directly; multiple items open the batch [export](/usage/exports) dialog),
- **Mark as reviewed** or **Mark as unreviewed**, and
- **Delete** them (admins only).
-
-## Filtering and the calendar
-
-Use the filter controls in the header to narrow what's shown. The available filters depend on the tab: Alerts and Detections can be filtered by **cameras**, **date**, **labels**, **zones**, and whether items are already reviewed; the Motion tab can be filtered by **cameras**, **date**, and **motion only**.
-
-The **calendar** filter lets you jump to a specific day (it shows **Last 24 Hours** until you pick one). On each day:
-
- An **underline** under the day number means **recordings exist** for that day. Days without recordings are dimmed.
- A **colored dot** under the day number means there is **unreviewed activity** that day: a **red dot** for unreviewed alerts, or an **orange dot** for unreviewed detections when there are no unreviewed alerts. Motion is not represented by a dot.
-
-Future dates are disabled, and the week start and time zone follow your configuration.
-
-## Reviewing Motion
-
-The Review page also can show periods of motion that didn't produce a tracked object, and provides a way to search past recordings for motion in a specific region. These tools complement the alerts and detections workflow above. See [Tuning Motion Detection](/configuration/motion_detection) for how the underlying motion detector is configured.
-
-The **Motion** tab itself shows a multi-camera grid scrubbed to a shared point in time, with a draggable timeline and a playback-speed selector. A camera tile gains a colored ring when a review item or significant motion overlaps the current time, and clicking a tile opens that camera's recording at that moment. Each camera's options menu (the kebab in the corner of its tile) is where you open **Motion Previews** and **Motion Search**, described below.
-
-### Motion Previews
-
-The Motion Previews pane shows preview clips for periods of significant motion that did not produce a tracked object. It is useful for spotting things that motion detection picked up but object detection did not, which can help validate tuning or catch missed objects.
-
-On the <NavPath path="Review > Motion" /> page, click the kebab menu on a camera and choose **Motion Previews**. Each card represents a continuous range of motion-only activity and plays back the recorded preview for that range. A heatmap overlay dims areas of the frame with no motion so the moving regions stand out.
-
-The pane provides a few controls:
-
- **Speed**: speeds up or slows down all of the preview clips at once.
- **Dim**: controls how strongly non-motion areas are darkened by the heatmap overlay. Higher values increase motion area visibility.
- **Filter**: opens a 16×16 grid overlaid on a snapshot of the camera. Select one or more cells to only show clips with motion in those regions. This is helpful for filtering out motion in areas like a busy street while keeping motion in your driveway.
-
-Clicking a preview clip seeks the recording player to that timestamp so you can review the full footage.
-
-### Motion Search
-
-Motion Search lets you scan recorded footage for changes inside a region of interest you draw on the camera. Unlike Motion Previews, which surfaces what Frigate's motion detector flagged in real time, Motion Search re-analyzes the saved recordings, so it can find changes that were missed (for example, an object that appeared while motion detection was paused by `lightning_threshold`, or in a region that is normally motion-masked).
-
-To start a search, open the Actions menu in [History](/usage/history) or click the kebab menu on a camera in the <NavPath path="Review > Motion" /> page and choose **Motion Search**. In the dialog:
-
-1. Pick the camera and time range to scan. In the date pickers, days that have recordings available are underlined.
-2. Draw a polygon on the camera frame to define the region of interest.
-3. Adjust the search parameters if needed:
-
-| Field                     | Description                                                                                                                                                                                                                                                                                                                   |
-| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Sensitivity Threshold** | Per-pixel luminance change required to count as motion inside the ROI. Behaves like Frigate's motion detection `threshold` setting.                                                                                                                                                                                           |
-| **Minimum Change Area**   | Minimum size of a single moving region, as a percentage of the ROI, for a frame to count as significant. Raise it to ignore small movements (leaves, distant motion); lower it when your subject covers only a small slice of the ROI. Every result shows the percentage it scored, so you can use those values to tune this. |
-| **Maximum Results**       | Maximum number of matching timestamps to return. The search stops once it reaches this many results, so a lower value finishes sooner while a higher value scans further into the range.                                                                                                                                      |
-| **Parallel mode**         | Decode multiple recording ranges at the same time. Speeds up large time ranges at the cost of higher decoding and CPU usage.                                                                                                                                                                                                  |
-
-Motion Search samples each recording's keyframes automatically, so there is no frame-rate or sampling setting to tune.
-
-Once running, Frigate scans the recording segments that overlap the time range and reports timestamps where changes were detected inside the polygon, along with the percentage of the ROI that changed. Clicking a result seeks the player to that moment so you can review what happened.
-
-The results panel shows the time range being scanned, a live progress bar with the timestamp currently being analyzed, and the running result count. A collapsible **Search Metrics** section reports how many segments were scanned and processed, how many were skipped because no motion was recorded in the ROI (using the stored motion heatmap), how many frames were decoded, and the total search time. Skipping segments with no recorded motion in the selected ROI is what makes searching long time ranges practical.
-
-#### Common use cases
-
-Frigate's main use case is to record and surface tracked objects, so Motion Search is most useful for the cases where object detection produced nothing: there is no object to find in Explore, but you suspect something happened.
-
- **Locating an unattributed change.** You know something appeared, disappeared, or moved in a window of footage (a package now gone, a gate left open), but no detection points to it. A search returns the candidate timestamps instead of scrubbing the timeline by hand.
- **An object that was never detected.** Something Frigate doesn't have a model label for, an object too small or distant to be detected, or movement in a region where detection isn't running. The activity left no tracked object but did change the pixels, so a search can still find it.
- **Activity while detection was effectively paused.** Changes that occurred while object detection was disabled, motion was suppressed by `skip_motion_threshold`, or inside an area covered by a motion mask, won't appear as review items or tracked objects but can be recovered by searching the recordings directly.
-
-#### Examples
-
-These show how to choose the ROI and **Minimum Change Area** for two common goals. Minimum Change Area is the size of a single moving region as a percentage of the ROI you draw, so the right value depends on how much of the ROI your subject, and its movement between samples, covers.
-
-Because samples are a second or more apart, a moving subject usually appears in two places at once in the comparison, so even ordinary motion often scores tens of percent and a low threshold lets in almost everything. The most reliable approach is to **run a search, look at the percentage each result scored, and set Minimum Change Area just below the values for the events you care about.** The default is 20%; the suggestions below are starting points.
-
- **When did this item first appear (or disappear)?** A package was dropped off, a car parked, or a trash can was moved, and you want the exact moment. Draw a **tight ROI** around the spot the item occupies and **raise Minimum Change Area** (start around 40–60%). Because the item fills most of a tight ROI, its arrival or removal is a large change, while smaller nearby motion (shadows, a passing pedestrian) stays below the threshold. The **earliest result** is when it appeared; if you only care about that moment, a low Maximum Results finishes faster. If you get no hits, the ROI is probably looser than the item: lower the threshold or tighten the ROI.
- **What's been getting into the garden?** Something has been trampling a flower bed overnight and no object was ever tracked. Draw a **looser ROI** covering the whole bed and use a **lower Minimum Change Area than the case above**: start near the 20% default and lower it (toward 5–10%) only if a small or distant subject is missed, since it covers just a slice of a large region. Expect more results to scan through: step through the timestamps and jump to each to see what triggered it. If wind-blown plants add noise, raise Minimum Change Area or the Sensitivity Threshold.
-
-#### Expected performance
-
-Motion Search analyzes the saved recordings on demand rather than reading a pre-built index, so a search over a long range takes longer than browsing Motion Previews. Cost scales mainly with how much footage has to be examined: segments with no recorded motion in your ROI are skipped using the stored motion heatmap (shown as "segments skipped" in the status panel), so a quiet range finishes quickly while a busy one takes longer.
-
-To increase the speed of searches:
-
- Draw a tight ROI. Because **Minimum Change Area** is measured as a percentage of the region you draw, a tight ROI around where you expect the change makes the object fill a larger share of the area, so it clears the threshold more easily. A loose ROI makes the same object a small fraction of the region, so it can fall below the threshold and be missed, forcing you to lower Minimum Change Area, which lets in more noise.
- Narrow the time range to the window you care about, so there is less footage to examine.
- Lower **Maximum Results** when you only need the first few hits. Because the search stops once it reaches that many results, a smaller value lets a busy range finish early instead of scanning the whole window.
- Use Parallel mode to shorten wall-clock time on multi-core systems, at the cost of higher decoding and CPU usage while it runs.
-
-## AI review summaries
-
-When [Generative AI review](/configuration/genai/genai_review) is configured, Frigate can generate a title, description, and threat classification for review items and surface them automatically in Review and History. Clicking the summary chip opens an **AI Analysis** dialog with the generated detail and any flagged concerns.
-
-In Review, an additional icon appears on unreviewed items that the AI classified as **suspicious** (Level 1) or **critical** (Level 2), so the activity that most warrants attention stands out before you open it. The icon goes away once the item has been reviewed.
@@ -186,7 +186,6 @@ const config: Config = {
  },
  plugins: [
    path.resolve(__dirname, "plugins", "raw-loader"),
-    path.resolve(__dirname, "plugins", "yaml-loader"),
    [
      "docusaurus-plugin-openapi-docs",
      {
@@ -11,7 +11,7 @@
        "@docusaurus/core": "^3.7.0",
        "@docusaurus/plugin-content-docs": "^3.7.0",
        "@docusaurus/preset-classic": "^3.7.0",
-        "@docusaurus/theme-mermaid": "^3.10.2",
+        "@docusaurus/theme-mermaid": "^3.7.0",
        "@inkeep/docusaurus": "^2.0.16",
        "@mdx-js/react": "^3.1.0",
        "@types/js-yaml": "^4.0.9",
@@ -19,7 +19,6 @@
        "docusaurus-plugin-openapi-docs": "^4.5.1",
        "docusaurus-theme-openapi-docs": "^4.5.1",
        "js-yaml": "^4.1.1",
-        "marked": "^16.4.2",
        "prism-react-renderer": "^2.4.1",
        "raw-loader": "^4.0.2",
        "react": "^18.3.1",
@@ -34,21 +33,6 @@
        "node": ">=18.0"
      }
    },
-    "node_modules/@11ty/gray-matter": {
-      "version": "1.0.0",
-      "resolved": "https://registry.npmjs.org/@11ty/gray-matter/-/gray-matter-1.0.0.tgz",
-      "integrity": "sha512-7mJJl+wf1AByoT0PknQiQfOPnVNT4fevGrUBVWO4HXsnYn1aQPyRyrELYrNUFleUBM++KzMKN6QaxHPk0t/6/g==",
-      "license": "MIT",
-      "dependencies": {
-        "js-yaml": "^4.1.0",
-        "kind-of": "^6.0.3",
-        "section-matter": "^1.0.0",
-        "strip-bom-string": "^1.0.0"
-      },
-      "engines": {
-        "node": ">=11"
-      }
-    },
    "node_modules/@ai-sdk/gateway": {
      "version": "2.0.20",
      "resolved": "https://registry.npmjs.org/@ai-sdk/gateway/-/gateway-2.0.20.tgz",
@@ -4022,16 +4006,16 @@
      }
    },
    "node_modules/@docusaurus/theme-mermaid": {
-      "version": "3.10.2",
-      "resolved": "https://registry.npmjs.org/@docusaurus/theme-mermaid/-/theme-mermaid-3.10.2.tgz",
-      "integrity": "sha512-Stssh5MYQJ+EdYugUXf+ZcpeJFQPKXf0KCd/SWp10o3CmXNaOoh5IEgVjVqY1e1XhQf3on4+Y4BnrMiD95E2SQ==",
+      "version": "3.9.2",
+      "resolved": "https://registry.npmjs.org/@docusaurus/theme-mermaid/-/theme-mermaid-3.9.2.tgz",
+      "integrity": "sha512-5vhShRDq/ntLzdInsQkTdoKWSzw8d1jB17sNPYhA/KvYYFXfuVEGHLM6nrf8MFbV8TruAHDG21Fn3W4lO8GaDw==",
      "license": "MIT",
      "dependencies": {
-        "@docusaurus/core": "3.10.2",
-        "@docusaurus/module-type-aliases": "3.10.2",
-        "@docusaurus/theme-common": "3.10.2",
-        "@docusaurus/types": "3.10.2",
-        "@docusaurus/utils-validation": "3.10.2",
+        "@docusaurus/core": "3.9.2",
+        "@docusaurus/module-type-aliases": "3.9.2",
+        "@docusaurus/theme-common": "3.9.2",
+        "@docusaurus/types": "3.9.2",
+        "@docusaurus/utils-validation": "3.9.2",
        "mermaid": ">=11.6.0",
        "tslib": "^2.6.0"
      },
@@ -4049,407 +4033,6 @@
        }
      }
    },
-    "node_modules/@docusaurus/theme-mermaid/node_modules/@docusaurus/babel": {
-      "version": "3.10.2",
-      "resolved": "https://registry.npmjs.org/@docusaurus/babel/-/babel-3.10.2.tgz",
-      "integrity": "sha512-aJ1hpGyvfkte3dDAfNbWM4biW4yWZBVz7TIGLZP+v+tWOBgxX3e0N5ZIXHIvmfNNXTI77pcHUx3KmtOk05Ze3Q==",
-      "license": "MIT",
-      "dependencies": {
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-        "@babel/generator": "^7.25.9",
-        "@babel/plugin-syntax-dynamic-import": "^7.8.3",
-        "@babel/plugin-transform-runtime": "^7.25.9",
-        "@babel/preset-env": "^7.25.9",
-        "@babel/preset-react": "^7.25.9",
-        "@babel/preset-typescript": "^7.25.9",
-        "@babel/runtime": "^7.25.9",
-        "@babel/traverse": "^7.25.9",
-        "@docusaurus/logger": "3.10.2",
-        "@docusaurus/utils": "3.10.2",
-        "babel-plugin-dynamic-import-node": "^2.3.3",
-        "fs-extra": "^11.1.1",
-        "tslib": "^2.6.0"
-      },
-      "engines": {
-        "node": ">=20.0"
-      }
-    },
-    "node_modules/@docusaurus/theme-mermaid/node_modules/@docusaurus/bundler": {
-      "version": "3.10.2",
-      "resolved": "https://registry.npmjs.org/@docusaurus/bundler/-/bundler-3.10.2.tgz",
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-        "@docusaurus/babel": "3.10.2",
-        "@docusaurus/cssnano-preset": "3.10.2",
-        "@docusaurus/logger": "3.10.2",
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-        "clean-css": "^5.3.3",
-        "copy-webpack-plugin": "^11.0.0",
-        "css-loader": "^6.11.0",
-        "css-minimizer-webpack-plugin": "^5.0.1",
-        "cssnano": "^6.1.2",
-        "file-loader": "^6.2.0",
-        "html-minifier-terser": "^7.2.0",
-        "mini-css-extract-plugin": "^2.9.2",
-        "null-loader": "^4.0.1",
-        "postcss": "^8.5.4",
-        "postcss-loader": "^7.3.4",
-        "postcss-preset-env": "^10.2.1",
-        "terser-webpack-plugin": "^5.3.9",
-        "tslib": "^2.6.0",
-        "url-loader": "^4.1.1",
-        "webpack": "^5.95.0",
-        "webpackbar": "^7.0.0"
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-      "engines": {
-        "node": ">=20.0"
-      },
-      "peerDependencies": {
-        "@docusaurus/faster": "*"
-      },
-      "peerDependenciesMeta": {
-        "@docusaurus/faster": {
-          "optional": true
-        }
-      }
-    },
-    "node_modules/@docusaurus/theme-mermaid/node_modules/@docusaurus/core": {
-      "version": "3.10.2",
-      "resolved": "https://registry.npmjs.org/@docusaurus/core/-/core-3.10.2.tgz",
-      "integrity": "sha512-EYByj6nk+aD9KeVxV6Hmo2/nAAT79P21Y82ycTBOBtrmqilloIbIEhgL2/8Xpt2Jz/pgNqHAwyusOGwmbKeJmA==",
-      "license": "MIT",
-      "dependencies": {
-        "@docusaurus/babel": "3.10.2",
-        "@docusaurus/bundler": "3.10.2",
-        "@docusaurus/logger": "3.10.2",
-        "@docusaurus/mdx-loader": "3.10.2",
-        "@docusaurus/utils": "3.10.2",
-        "@docusaurus/utils-common": "3.10.2",
-        "@docusaurus/utils-validation": "3.10.2",
-        "boxen": "^6.2.1",
-        "chalk": "^4.1.2",
-        "chokidar": "^3.5.3",
-        "cli-table3": "^0.6.3",
-        "combine-promises": "^1.1.0",
-        "commander": "^5.1.0",
-        "core-js": "^3.31.1",
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-        "detect-port": "dist/commonjs/bin/detect-port.js"
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-      "engines": {
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-      "peerDependencies": {
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-        "webpack": "3 || 4 || 5"
-      },
-      "peerDependenciesMeta": {
-        "@rspack/core": {
-          "optional": true
-        },
-        "webpack": {
-          "optional": true
-        }
-      }
-    },
    "node_modules/@docusaurus/theme-search-algolia": {
      "version": "3.9.2",
      "resolved": "https://registry.npmjs.org/@docusaurus/theme-search-algolia/-/theme-search-algolia-3.9.2.tgz",
@@ -6892,15 +6475,6 @@
        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
      }
    },
-    "node_modules/ansis": {
-      "version": "3.17.0",
-      "resolved": "https://registry.npmjs.org/ansis/-/ansis-3.17.0.tgz",
-      "integrity": "sha512-0qWUglt9JEqLFr3w1I1pbrChn1grhaiAR2ocX1PP/flRmxgtwTzPFFFnfIlD6aMOLQZgSuCRlidD70lvx8yhzg==",
-      "license": "ISC",
-      "engines": {
-        "node": ">=14"
-      }
-    },
    "node_modules/any-promise": {
      "version": "1.3.0",
      "resolved": "https://registry.npmjs.org/any-promise/-/any-promise-1.3.0.tgz",
@@ -19244,9 +18818,9 @@
      }
    },
    "node_modules/react-loadable-ssr-addon-v5-slorber": {
-      "version": "1.0.3",
-      "resolved": "https://registry.npmjs.org/react-loadable-ssr-addon-v5-slorber/-/react-loadable-ssr-addon-v5-slorber-1.0.3.tgz",
-      "integrity": "sha512-GXfh9VLwB5ERaCsU6RULh7tkemeX15aNh6wuMEBtfdyMa7fFG8TXrhXlx1SoEK2Ty/l6XIkzzYIQmyaWW3JgdQ==",
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/react-loadable-ssr-addon-v5-slorber/-/react-loadable-ssr-addon-v5-slorber-1.0.1.tgz",
+      "integrity": "sha512-lq3Lyw1lGku8zUEJPDxsNm1AfYHBrO9Y1+olAYwpUJ2IGFBskM0DMKok97A6LWUpHm+o7IvQBOWu9MLenp9Z+A==",
      "license": "MIT",
      "dependencies": {
        "@babel/runtime": "^7.10.3"
@@ -21027,24 +20601,24 @@
      }
    },
    "node_modules/serve-handler": {
-      "version": "6.1.7",
-      "resolved": "https://registry.npmjs.org/serve-handler/-/serve-handler-6.1.7.tgz",
-      "integrity": "sha512-CinAq1xWb0vR3twAv9evEU8cNWkXCb9kd5ePAHUKJBkOsUpR1wt/CvGdeca7vqumL1U5cSaeVQ6zZMxiJ3yWsg==",
+      "version": "6.1.6",
+      "resolved": "https://registry.npmjs.org/serve-handler/-/serve-handler-6.1.6.tgz",
+      "integrity": "sha512-x5RL9Y2p5+Sh3D38Fh9i/iQ5ZK+e4xuXRd/pGbM4D13tgo/MGwbttUk8emytcr1YYzBYs+apnUngBDFYfpjPuQ==",
      "license": "MIT",
      "dependencies": {
        "bytes": "3.0.0",
        "content-disposition": "0.5.2",
        "mime-types": "2.1.18",
-        "minimatch": "3.1.5",
+        "minimatch": "3.1.2",
        "path-is-inside": "1.0.2",
        "path-to-regexp": "3.3.0",
        "range-parser": "1.2.0"
      }
    },
    "node_modules/serve-handler/node_modules/brace-expansion": {
-      "version": "1.1.16",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.16.tgz",
-      "integrity": "sha512-IDw48K2/2kRkg9LdJxurvq3lV3aBgq0REY89duEqFRthjlPdXHKMj7EnQOXVckxzgisinf3nHfrcE2FufFLXMw==",
+      "version": "1.1.12",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz",
+      "integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==",
      "license": "MIT",
      "dependencies": {
        "balanced-match": "^1.0.0",
@@ -21073,9 +20647,9 @@
      }
    },
    "node_modules/serve-handler/node_modules/minimatch": {
-      "version": "3.1.5",
-      "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.5.tgz",
-      "integrity": "sha512-VgjWUsnnT6n+NUk6eZq77zeFdpW2LWDzP6zFGrCbHXiYNul5Dzqk2HHQ5uFH2DNW5Xbp8+jVzaeNt94ssEEl4w==",
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz",
+      "integrity": "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==",
      "license": "ISC",
      "dependencies": {
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@@ -21,7 +21,7 @@
    "@docusaurus/core": "^3.7.0",
    "@docusaurus/plugin-content-docs": "^3.7.0",
    "@docusaurus/preset-classic": "^3.7.0",
-    "@docusaurus/theme-mermaid": "^3.10.2",
+    "@docusaurus/theme-mermaid": "^3.7.0",
    "@inkeep/docusaurus": "^2.0.16",
    "@mdx-js/react": "^3.1.0",
    "@types/js-yaml": "^4.0.9",
@@ -29,7 +29,6 @@
    "docusaurus-plugin-openapi-docs": "^4.5.1",
    "docusaurus-theme-openapi-docs": "^4.5.1",
    "js-yaml": "^4.1.1",
-    "marked": "^16.4.2",
    "prism-react-renderer": "^2.4.1",
    "raw-loader": "^4.0.2",
    "react": "^18.3.1",
@@ -1,9 +0,0 @@
-const yaml = require("js-yaml");
-
-// Webpack loader that compiles a YAML file into a default-exported JS object,
-// so docs data can be authored in YAML (block scalars, no quote/newline
-// escaping) but imported exactly like the JSON it replaces.
-module.exports = function (source) {
-  const data = yaml.load(source);
-  return `export default ${JSON.stringify(data)};`;
-};
@@ -1,23 +0,0 @@
-const path = require("node:path");
-
-// Enables importing YAML data files from docs/data as plain JS objects.
-// Scoped to the data directory so it never intercepts other .yaml files
-// (e.g. the OpenAPI spec under static/).
-module.exports = function (context, options) {
-  return {
-    name: "yaml-data-loader",
-    configureWebpack(config, isServer, utils) {
-      return {
-        module: {
-          rules: [
-            {
-              test: /\.ya?ml$/,
-              include: path.resolve(__dirname, "..", "data"),
-              use: path.resolve(__dirname, "js-yaml-loader.js"),
-            },
-          ],
-        },
-      };
-    },
-  };
-};
@@ -17,126 +17,91 @@ const sidebars: SidebarsConfig = {
    ],
    Guides: [
      "guides/getting_started",
+      "guides/configuring_go2rtc",
      "guides/ha_notifications",
      "guides/ha_network_storage",
      "guides/reverse_proxy",
    ],
-    Usage: [
-      "usage/live",
-      "usage/review",
-      "usage/history",
-      "usage/explore",
-      "usage/exports",
-    ],
-    Configuration: [
-      "configuration/config",
-      {
-        type: "category",
-        label: "Detectors",
-        items: [
-          "configuration/object_detectors",
-          "configuration/audio_detectors",
-        ],
-      },
-      {
-        type: "category",
-        label: "Enrichments",
-        items: [
-          "configuration/semantic_search",
-          "configuration/face_recognition",
-          "configuration/license_plate_recognition",
-          "configuration/bird_classification",
-          {
-            type: "category",
-            label: "Custom Classification",
-            link: {
-              type: "generated-index",
-              title: "Custom Classification",
-              description: "Configuration for custom classification models",
-            },
-            items: [
-              "configuration/custom_classification/state_classification",
-              "configuration/custom_classification/object_classification",
-            ],
+    Configuration: {
+      "Configuration Files": [
+        "configuration/index",
+        "configuration/reference",
+        {
+          type: "link",
+          label: "Go2RTC Configuration Reference",
+          href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.13#configuration",
+        } as PropSidebarItemLink,
+      ],
+      Detectors: [
+        "configuration/object_detectors",
+        "configuration/audio_detectors",
+      ],
+      Enrichments: [
+        "configuration/semantic_search",
+        "configuration/face_recognition",
+        "configuration/license_plate_recognition",
+        "configuration/bird_classification",
+        {
+          type: "category",
+          label: "Custom Classification",
+          link: {
+            type: "generated-index",
+            title: "Custom Classification",
+            description: "Configuration for custom classification models",
          },
-          {
-            type: "category",
-            label: "Generative AI",
-            link: {
-              type: "generated-index",
-              title: "Generative AI",
-              description: "Generative AI Features",
-            },
-            items: [
-              "configuration/genai/genai_config",
-              "configuration/genai/genai_review",
-              "configuration/genai/genai_objects",
-            ],
+          items: [
+            "configuration/custom_classification/state_classification",
+            "configuration/custom_classification/object_classification",
+          ],
+        },
+        {
+          type: "category",
+          label: "Generative AI",
+          link: {
+            type: "generated-index",
+            title: "Generative AI",
+            description: "Generative AI Features",
          },
-        ],
-      },
-      {
-        type: "category",
-        label: "Cameras",
-        items: [
-          "configuration/cameras",
-          "configuration/review",
-          "configuration/record",
-          "configuration/snapshots",
-          "configuration/motion_detection",
-          "configuration/birdseye",
-          "configuration/live",
-          "configuration/restream",
-          "configuration/autotracking",
-          "configuration/camera_specific",
-        ],
-      },
-      {
-        type: "category",
-        label: "Objects",
-        items: [
-          "configuration/object_filters",
-          "configuration/masks",
-          "configuration/zones",
-          "configuration/objects",
-          "configuration/stationary_objects",
-        ],
-      },
-      {
-        type: "category",
-        label: "Hardware Acceleration",
-        items: [
-          "configuration/hardware_acceleration_video",
-          "configuration/hardware_acceleration_enrichments",
-        ],
-      },
-      {
-        type: "category",
-        label: "Extra Configuration",
-        items: [
-          "configuration/authentication",
-          "configuration/notifications",
-          "configuration/profiles",
-          "configuration/go2rtc",
-          "configuration/ffmpeg_presets",
-          "configuration/pwa",
-          "configuration/tls",
-        ],
-      },
-      {
-        type: "category",
-        label: "Advanced Configuration",
-        items: [
-          "configuration/advanced/system",
-          "configuration/advanced/reference",
-          {
-            type: "link",
-            label: "Go2RTC Configuration Reference",
-            href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.14#configuration",
-          } as PropSidebarItemLink,
-        ],
-      },
-    ],
+          items: [
+            "configuration/genai/genai_config",
+            "configuration/genai/genai_review",
+            "configuration/genai/genai_objects",
+          ],
+        },
+      ],
+      Cameras: [
+        "configuration/cameras",
+        "configuration/review",
+        "configuration/record",
+        "configuration/snapshots",
+        "configuration/motion_detection",
+        "configuration/birdseye",
+        "configuration/live",
+        "configuration/restream",
+        "configuration/autotracking",
+        "configuration/camera_specific",
+      ],
+      Objects: [
+        "configuration/object_filters",
+        "configuration/masks",
+        "configuration/zones",
+        "configuration/objects",
+        "configuration/stationary_objects",
+      ],
+      "Hardware Acceleration": [
+        "configuration/hardware_acceleration_video",
+        "configuration/hardware_acceleration_enrichments",
+      ],
+      "Extra Configuration": [
+        "configuration/authentication",
+        "configuration/notifications",
+        "configuration/profiles",
+        "configuration/ffmpeg_presets",
+        "configuration/pwa",
+        "configuration/tls",
+        "configuration/advanced",
+      ],
+    },
    Integrations: [
      "integrations/plus",
      "integrations/home-assistant",
@@ -165,7 +130,6 @@ const sidebars: SidebarsConfig = {
    ],
    Troubleshooting: [
      "troubleshooting/faqs",
-      "troubleshooting/go2rtc",
      "troubleshooting/recordings",
      "troubleshooting/dummy-camera",
      {
@@ -1,66 +0,0 @@
-import React, { useState, useEffect } from "react";
-import Heading from "@theme/Heading";
-import styles from "./styles.module.css";
-
-// A single FAQ entry.
-//
-// The question is a real anchored heading (via @theme/Heading), so on desktop
-// it gets the standard hover "#" hash link and the answer is always shown. On
-// mobile the heading text is a button that toggles its answer, keeping long
-// FAQ pages short. The desktop/mobile split is pure CSS (Docusaurus breakpoint:
-// 996px), so there is no hydration flash. The answer is always rendered into
-// the DOM, so search engines and the docs AI bot can read it regardless of
-// layout or collapsed state. The heading id resolves deep links on both layouts
-// and auto-expands the entry on mobile when it is the link target.
-export default function FaqItem({ id, question, children }) {
-  const [open, setOpen] = useState(false);
-
-  useEffect(() => {
-    const openIfTargeted = () => {
-      if (window.location.hash === `#${id}`) {
-        setOpen(true);
-      }
-    };
-    openIfTargeted();
-    window.addEventListener("hashchange", openIfTargeted);
-    return () => window.removeEventListener("hashchange", openIfTargeted);
-  }, [id]);
-
-  const toggle = () => {
-    const next = !open;
-    setOpen(next);
-    // Reflect the entry in the URL like clicking the heading anchor, so an
-    // opened answer is shareable. Use replaceState to avoid history spam and
-    // an abrupt scroll. Clear it on close if it currently points here.
-    if (next) {
-      if (window.location.hash !== `#${id}`) {
-        window.history.replaceState(null, "", `#${id}`);
-      }
-    } else if (window.location.hash === `#${id}`) {
-      window.history.replaceState(
-        null,
-        "",
-        window.location.pathname + window.location.search,
-      );
-    }
-  };
-
-  return (
-    <div className={styles.item} data-open={open || undefined}>
-      <Heading as="h4" id={id} className={styles.heading}>
-        <button
-          type="button"
-          className={styles.toggle}
-          aria-expanded={open}
-          aria-controls={`${id}-content`}
-          onClick={toggle}
-        >
-          {question}
-        </button>
-      </Heading>
-      <div id={`${id}-content`} className={styles.content}>
-        {children}
-      </div>
-    </div>
-  );
-}
@@ -1,87 +0,0 @@
-/*
- * FAQ entry: collapsible on mobile, static heading + expanded answer on
- * desktop. The split is pure CSS (Docusaurus breakpoint: 996px) so there is
- * no hydration flash. The answer is always rendered into the DOM, so search
- * engines and the docs AI bot can read it regardless of layout or state.
- */
-
-.item {
-  scroll-margin-top: calc(var(--ifm-navbar-height) + 1rem);
-}
-
-.heading {
-  margin: 0;
-}
-
-/* Mobile: the heading text is a full-width clickable toggle row. */
-.toggle {
-  display: flex;
-  align-items: center;
-  gap: 0.6rem;
-  width: 100%;
-  padding: 0.85rem 0;
-  border: none;
-  border-bottom: 1px solid var(--ifm-color-emphasis-200);
-  background: none;
-  color: inherit;
-  font: inherit;
-  text-align: left;
-  cursor: pointer;
-}
-
-.toggle::before {
-  content: "";
-  flex: 0 0 auto;
-  width: 0.5rem;
-  height: 0.5rem;
-  border-right: 2px solid currentColor;
-  border-bottom: 2px solid currentColor;
-  transform: rotate(-45deg);
-  transition: transform var(--ifm-transition-fast, 200ms) ease;
-}
-
-.item[data-open] .toggle::before {
-  transform: rotate(45deg);
-}
-
-.content {
-  display: none;
-  padding: 0 0 0.85rem;
-}
-
-.item[data-open] .content {
-  display: block;
-}
-
-/* Hide the hover hash link on mobile (no hover; avoids a stray empty line). */
-.heading :global(.hash-link) {
-  display: none;
-}
-
-/* Desktop: render as a normal expanded heading + answer. */
-@media (min-width: 997px) {
-  .heading {
-    margin: 1.75rem 0 0.5rem;
-  }
-
-  .toggle {
-    display: inline;
-    width: auto;
-    padding: 0;
-    border: none;
-    cursor: default;
-  }
-
-  .toggle::before {
-    display: none;
-  }
-
-  .content {
-    display: block;
-    padding: 0;
-  }
-
-  .heading :global(.hash-link) {
-    display: inline;
-  }
-}
@@ -1,171 +0,0 @@
-import React, { useState } from "react";
-import CodeBlock from "@theme/CodeBlock";
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import { marked } from "marked";
-import styles from "./styles.module.css";
-
-marked.setOptions({ gfm: true });
-
-/**
- * @typedef {Object} Model
- * @property {string} key
- * @property {string} label
- * @property {boolean} recommended
- * @property {string} download Markdown for the "download the model" step.
- * @property {string} ui Markdown for the Frigate UI configuration step.
- * @property {string} yaml Raw YAML for the configuration step.
- */
-
-// Render a markdown string to React nodes. Fenced code blocks become Docusaurus
-// CodeBlock components (so they get syntax highlighting and a copy button);
-// everything else is marked-parsed to HTML.
-function renderBlocks(md, keyPrefix) {
-  if (!md.trim()) return [];
-  const tokens = marked.lexer(md);
-  const nodes = [];
-  let buffer = [];
-  let idx = 0;
-
-  const flush = () => {
-    if (buffer.length) {
-      buffer.links = tokens.links;
-      nodes.push(
-        <div
-          key={`${keyPrefix}-h${idx++}`}
-          dangerouslySetInnerHTML={{ __html: marked.parser(buffer) }}
-        />,
-      );
-      buffer = [];
-    }
-  };
-
-  tokens.forEach((token) => {
-    if (token.type === "code") {
-      flush();
-      const language = (token.lang || "text").split(/\s+/)[0];
-      nodes.push(
-        <CodeBlock key={`${keyPrefix}-c${idx++}`} language={language}>
-          {token.text}
-        </CodeBlock>,
-      );
-    } else {
-      buffer.push(token);
-    }
-  });
-  flush();
-  return nodes;
-}
-
-// marked does not understand Docusaurus admonitions (:::warning ... :::), so
-// render those blocks ourselves and render everything around them normally.
-function renderMarkdown(md) {
-  if (!md) return null;
-  const admonition = /:::(\w+)[ \t]*([^\n]*)\n([\s\S]*?)\n:::/g;
-  const nodes = [];
-  let lastIndex = 0;
-  let match;
-  let k = 0;
-  while ((match = admonition.exec(md)) !== null) {
-    nodes.push(...renderBlocks(md.slice(lastIndex, match.index), `seg${k}`));
-    const [, type, title, body] = match;
-    const heading = (title || type).trim();
-    nodes.push(
-      <div
-        key={`adm${k}`}
-        className={`${styles.admonition} ${styles[`admonition_${type}`] || ""}`}
-      >
-        <div className={styles.admonitionTitle}>{heading}</div>
-        {renderBlocks(body, `adm${k}`)}
-      </div>,
-    );
-    lastIndex = admonition.lastIndex;
-    k++;
-  }
-  nodes.push(...renderBlocks(md.slice(lastIndex), `seg${k}`));
-  return nodes;
-}
-
-function Markdown({ children }) {
-  return <div className={styles.markdown}>{renderMarkdown(children)}</div>;
-}
-
-function RecommendedBadge() {
-  return <span className={styles.recommendedBadge}>Recommended</span>;
-}
-
-/**
- * @param {{ models: Model[] }} props
- */
-export default function ModelConfigDropdown({ models }) {
-  const [selectedModelIndex, setSelectedModelIndex] = useState(0);
-  const [isOpen, setIsOpen] = useState(false);
-
-  const selectedModel = models[selectedModelIndex];
-  const hasChoices = models.length > 1;
-
-  const handleModelSelect = (index) => {
-    setSelectedModelIndex(index);
-    setIsOpen(false);
-  };
-
-  return (
-    <div className={styles.wrapper}>
-      <div className={styles.panel}>
-        <div className={styles.step}>
-          <h4 className={styles.stepTitle}>Step 1 — Choose a model</h4>
-          <div
-            className={`${styles.dropdown} ${isOpen ? styles.open : ""} ${
-              hasChoices ? "" : styles.static
-            }`}
-            onClick={hasChoices ? () => setIsOpen(!isOpen) : undefined}
-          >
-            <div className={styles.dropdownContent}>
-              <span className={styles.modelName}>
-                {selectedModel.label}
-                {selectedModel.recommended && <RecommendedBadge />}
-              </span>
-              {hasChoices && (
-                <span className={styles.arrow}>{isOpen ? "▲" : "▼"}</span>
-              )}
-            </div>
-          </div>
-
-          {isOpen && hasChoices && (
-            <div className={styles.menu}>
-              {models.map((model, index) => (
-                <div
-                  key={model.key}
-                  className={`${styles.menuItem} ${
-                    index === selectedModelIndex ? styles.menuItemActive : ""
-                  }`}
-                  onClick={() => handleModelSelect(index)}
-                >
-                  {model.label}
-                  {model.recommended && <RecommendedBadge />}
-                </div>
-              ))}
-            </div>
-          )}
-        </div>
-
-        <div className={styles.step}>
-          <h4 className={styles.stepTitle}>Step 2 — Download the model</h4>
-          <Markdown>{selectedModel.download}</Markdown>
-        </div>
-
-        <div className={styles.step}>
-          <h4 className={styles.stepTitle}>Step 3 — Configure the detector</h4>
-          <ConfigTabs>
-            <TabItem value="ui">
-              <Markdown>{selectedModel.ui}</Markdown>
-            </TabItem>
-            <TabItem value="yaml">
-              <CodeBlock language="yaml">{selectedModel.yaml}</CodeBlock>
-            </TabItem>
-          </ConfigTabs>
-        </div>
-      </div>
-    </div>
-  );
-}
@@ -1,275 +0,0 @@
-/* ===================================================================
-   ModelConfigDropdown — styles
-   =================================================================== */
-
-.wrapper {
-  margin: 1.5rem 0;
-}
-
-/* --- Dropdown button --- */
-
-.dropdown {
-  display: inline-block;
-  width: 360px;
-  max-width: 100%;
-  text-align: left;
-  border: 1px solid var(--ifm-color-emphasis-400);
-  border-radius: 8px;
-  background: var(--ifm-background-color);
-  cursor: pointer;
-  transition:
-    border-color 0.2s,
-    box-shadow 0.2s;
-}
-
-[data-theme="light"] .dropdown {
-  border: 1px solid #d0d7de;
-  background: #fff;
-}
-
-[data-theme="dark"] .dropdown {
-  border: 1px solid var(--ifm-color-emphasis-300);
-  background: #21262d;
-}
-
-.dropdown:hover {
-  border-color: var(--ifm-color-primary);
-  box-shadow: 0 0 0 3px var(--ifm-color-primary-lightest);
-}
-
-[data-theme="dark"] .dropdown:hover {
-  box-shadow: 0 0 0 3px var(--ifm-color-primary-lightest);
-}
-
-.dropdown.open {
-  border-color: var(--ifm-color-primary);
-  box-shadow: 0 0 0 3px var(--ifm-color-primary-lightest);
-}
-
-[data-theme="dark"] .dropdown.open {
-  border-color: var(--ifm-color-primary);
-}
-
-/* Single-model detectors render the label without a clickable menu. */
-.dropdown.static {
-  cursor: default;
-}
-
-.dropdown.static:hover {
-  border-color: var(--ifm-color-emphasis-400);
-  box-shadow: none;
-}
-
-[data-theme="light"] .dropdown.static:hover {
-  border-color: #d0d7de;
-}
-
-[data-theme="dark"] .dropdown.static:hover {
-  border-color: var(--ifm-color-emphasis-300);
-}
-
-.dropdownContent {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  gap: 1rem;
-  padding: 0.8rem 1rem;
-}
-
-/* --- Model menu --- */
-
-.menu {
-  margin-top: 0.25rem;
-  width: 360px;
-  max-width: 100%;
-  border: 1px solid var(--ifm-color-emphasis-400);
-  border-radius: 8px;
-  overflow: hidden;
-  background: var(--ifm-background-color);
-}
-
-[data-theme="light"] .menu {
-  border: 1px solid #d0d7de;
-  background: #fff;
-}
-
-[data-theme="dark"] .menu {
-  border: 1px solid var(--ifm-color-emphasis-300);
-  background: #21262d;
-}
-
-.menuItem {
-  display: flex;
-  align-items: center;
-  gap: 0.5rem;
-  padding: 0.6rem 1rem;
-  cursor: pointer;
-  font-size: 0.95rem;
-  color: var(--ifm-font-color-base);
-  transition: background 0.15s;
-}
-
-.menuItem:not(:last-child) {
-  border-bottom: 1px solid var(--ifm-color-emphasis-200);
-}
-
-.menuItem:hover {
-  background: var(--ifm-color-emphasis-100);
-}
-
-.menuItemActive {
-  font-weight: var(--ifm-font-weight-semibold);
-  background: var(--ifm-color-primary-lightest);
-}
-
-[data-theme="dark"] .menuItem:hover {
-  background: #2b3139;
-}
-
-[data-theme="dark"] .menuItemActive {
-  background: #2b3139;
-}
-
-.modelName {
-  font-weight: var(--ifm-font-weight-semibold);
-  color: var(--ifm-font-color-base);
-  font-size: 1rem;
-  display: flex;
-  align-items: center;
-  gap: 0.5rem;
-  white-space: nowrap;
-}
-
-.recommendedBadge {
-  display: inline-block;
-  background: var(--ifm-color-success);
-  color: #fff;
-  font-size: 0.7rem;
-  font-weight: 600;
-  padding: 2px 8px;
-  border-radius: 12px;
-  text-transform: uppercase;
-  letter-spacing: 0.5px;
-}
-
-.arrow {
-  font-size: 0.7rem;
-  color: var(--ifm-font-color-secondary);
-  transition: transform 0.2s;
-}
-
-.dropdown.open .arrow {
-  transform: rotate(180deg);
-}
-
-/* --- Panel --- */
-
-.panel {
-  margin-top: 0.5rem;
-  border: 1px solid var(--ifm-color-emphasis-400);
-  border-radius: 8px;
-  overflow: hidden;
-  background: var(--ifm-background-color);
-}
-
-[data-theme="light"] .panel {
-  border: 1px solid #d0d7de;
-  background: #fff;
-}
-
-[data-theme="dark"] .panel {
-  border: 1px solid var(--ifm-color-emphasis-300);
-  background: #21262d;
-}
-
-/* --- Steps --- */
-
-.step {
-  padding: 1rem;
-}
-
-.step:not(:last-child) {
-  border-bottom: 1px solid var(--ifm-color-emphasis-200);
-}
-
-.stepTitle {
-  margin: 0 0 0.75rem 0;
-  font-size: 1rem;
-  font-weight: 600;
-  color: var(--ifm-font-color-base);
-}
-
-/* Rendered markdown (download + Frigate UI instructions). */
-
-.markdown {
-  font-size: 0.9rem;
-  line-height: 1.6;
-}
-
-.markdown > :last-child {
-  margin-bottom: 0;
-}
-
-.markdown a {
-  color: var(--ifm-color-primary);
-  text-decoration: underline;
-  text-underline-offset: 2px;
-}
-
-.markdown table {
-  display: table;
-  width: 100%;
-  margin: 0.75rem 0;
-  font-size: 0.85rem;
-}
-
-/* Docusaurus-style admonitions rendered from markdown. */
-
-.admonition {
-  margin: 0.75rem 0;
-  padding: 0.75rem 1rem;
-  border-left: 4px solid var(--ifm-color-info);
-  border-radius: 4px;
-  background: var(--ifm-color-info-contrast-background);
-  font-size: 0.85rem;
-}
-
-.admonition > :last-child {
-  margin-bottom: 0;
-}
-
-.admonitionTitle {
-  font-weight: 700;
-  text-transform: uppercase;
-  letter-spacing: 0.5px;
-  font-size: 0.75rem;
-  margin-bottom: 0.4rem;
-  color: var(--ifm-color-info);
-}
-
-.admonition_warning {
-  border-left-color: var(--ifm-color-warning);
-  background: var(--ifm-color-warning-contrast-background);
-}
-
-.admonition_warning .admonitionTitle {
-  color: var(--ifm-color-warning-dark);
-}
-
-.admonition_danger {
-  border-left-color: var(--ifm-color-danger);
-  background: var(--ifm-color-danger-contrast-background);
-}
-
-.admonition_danger .admonitionTitle {
-  color: var(--ifm-color-danger-dark);
-}
-
-.admonition_tip {
-  border-left-color: var(--ifm-color-success);
-  background: var(--ifm-color-success-contrast-background);
-}
-
-.admonition_tip .admonitionTitle {
-  color: var(--ifm-color-success-dark);
-}
@@ -4,6 +4,7 @@ import multiprocessing as mp
 import signal
 import sys
 import threading
+from typing import Union

 import ruamel.yaml
 from pydantic import ValidationError
@@ -53,7 +54,7 @@ def main() -> None:
        print("*************************************************************\n")
        # Attempt to get the original config file for line number tracking
        config_path = find_config_file()
-        with open(config_path) as f:
+        with open(config_path, "r") as f:
            yaml_config = ruamel.yaml.YAML()
            yaml_config.preserve_quotes = True
            full_config = yaml_config.load(f)
@@ -67,7 +68,7 @@ def main() -> None:

            try:
                for i, part in enumerate(error_path):
-                    key: int | str = (
+                    key: Union[int, str] = (
                        int(part) if isinstance(part, str) and part.isdigit() else part
                    )

@@ -12,7 +12,7 @@ from datetime import datetime, timedelta
 from functools import reduce
 from io import StringIO
 from pathlib import Path as FilePath
-from typing import Any
+from typing import Any, Dict, List, Optional

 import aiofiles
 import ruamel.yaml
@@ -43,7 +43,6 @@ from frigate.config.camera.updater import (
    CameraConfigUpdateEnum,
    CameraConfigUpdateTopic,
 )
-from frigate.const import REDACTED_CREDENTIAL_SENTINEL
 from frigate.ffmpeg_presets import FFMPEG_HWACCEL_VAAPI, _gpu_selector
 from frigate.genai import PROVIDERS, load_providers
 from frigate.jobs.media_sync import (
@@ -62,11 +61,7 @@ from frigate.util.builtin import (
    process_config_query_string,
    update_yaml_file_bulk,
 )
-from frigate.util.config import (
-    apply_section_update,
-    find_config_file,
-    redact_credential,
-)
+from frigate.util.config import apply_section_update, find_config_file
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
    get_nvidia_driver_info,
@@ -113,7 +108,7 @@ def version():
@router.get("/stats", dependencies=[Depends(allow_any_authenticated())])
 def stats(
    request: Request,
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
    stats_data = request.app.stats_emitter.get_latest_stats()

@@ -164,7 +159,7 @@ def metrics(request: Request):
    # Retrieve the latest statistics and update the Prometheus metrics
    stats = request.app.stats_emitter.get_latest_stats()
    # query DB for count of events by camera, label
-    event_counts: list[dict[str, Any]] = (
+    event_counts: List[Dict[str, Any]] = (
        Event.select(Event.camera, Event.label, fn.Count())
        .group_by(Event.camera, Event.label)
        .dicts()
@@ -250,7 +245,7 @@ async def genai_probe(body: GenAIProbeBody):
            asyncio.to_thread(client.list_models),
            timeout=_PROBE_OUTER_TIMEOUT_SECONDS,
        )
-    except TimeoutError:
+    except asyncio.TimeoutError:
        return JSONResponse(
            content={"success": False, "message": "Probe timed out"},
        )
@@ -289,24 +284,26 @@ def config(request: Request):
    if request.headers.get("remote-role") != "admin":
        config.pop("environment_vars", None)

-    # redact mqtt credentials
-    redact_credential(config["mqtt"], "password")
+    # remove mqtt credentials
+    config["mqtt"].pop("password", None)
+    config["mqtt"].pop("user", None)

-    # redact proxy secret
-    redact_credential(config["proxy"], "auth_secret")
+    # remove the proxy secret
+    config["proxy"].pop("auth_secret", None)

-    # redact genai api keys
-    for _genai_name, genai_cfg in config.get("genai", {}).items():
+    # remove genai api keys
+    for genai_name, genai_cfg in config.get("genai", {}).items():
        if isinstance(genai_cfg, dict):
-            redact_credential(genai_cfg, "api_key")
+            genai_cfg.pop("api_key", None)

    for camera_name, camera in request.app.frigate_config.cameras.items():
        camera_dict = config["cameras"][camera_name]

-        # redact onvif credentials
+        # remove onvif credentials
        onvif_dict = camera_dict.get("onvif", {})
        if onvif_dict:
-            redact_credential(onvif_dict, "password")
+            onvif_dict.pop("user", None)
+            onvif_dict.pop("password", None)

        # clean paths
        for input in camera_dict.get("ffmpeg", {}).get("inputs", []):
@@ -374,7 +371,7 @@ def config(request: Request):
        if model_path:
            model_json_path = FilePath(model_path).with_suffix(".json")
            try:
-                with open(model_json_path) as f:
+                with open(model_json_path, "r") as f:
                    model_plus_data = json.load(f)
                config["model"]["plus"] = model_plus_data
            except FileNotFoundError:
@@ -502,7 +499,7 @@ def config_raw():
            status_code=404,
        )

-    with open(config_file) as f:
+    with open(config_file, "r") as f:
        raw_config = f.read()
        f.close()

@@ -683,10 +680,6 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
            _restore_masked_camera_paths(body.config_data, request.app.frigate_config)
            updates = flatten_config_data(body.config_data)
            updates = {k: ("" if v is None else v) for k, v in updates.items()}
-            # Drop any field whose value is still the redaction sentinel
-            updates = {
-                k: v for k, v in updates.items() if v != REDACTED_CREDENTIAL_SENTINEL
-            }

        if not updates:
            return JSONResponse(
@@ -750,40 +743,6 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
                    settings,
                )

-                # detect resize also republishes motion + objects so other
-                # processes pick up the rebuilt masks, and fires refresh so
-                # the camera maintainer recycles the camera process to pick
-                # up the new ffmpeg cmd / SHM sizing
-                if field == "detect":
-                    cam_cfg = config.cameras.get(camera)
-                    if cam_cfg is not None:
-                        if cam_cfg.motion is not None:
-                            request.app.config_publisher.publish_update(
-                                CameraConfigUpdateTopic(
-                                    CameraConfigUpdateEnum.motion, camera
-                                ),
-                                cam_cfg.motion,
-                            )
-                        request.app.config_publisher.publish_update(
-                            CameraConfigUpdateTopic(
-                                CameraConfigUpdateEnum.objects, camera
-                            ),
-                            cam_cfg.objects,
-                        )
-                        if cam_cfg.zones:
-                            request.app.config_publisher.publish_update(
-                                CameraConfigUpdateTopic(
-                                    CameraConfigUpdateEnum.zones, camera
-                                ),
-                                cam_cfg.zones,
-                            )
-                        request.app.config_publisher.publish_update(
-                            CameraConfigUpdateTopic(
-                                CameraConfigUpdateEnum.refresh, camera
-                            ),
-                            cam_cfg,
-                        )
-
        return JSONResponse(
            content={"success": True, "message": "Config applied in-memory"},
            status_code=200,
@@ -807,7 +766,7 @@ def config_set(request: Request, body: AppConfigSetBody):

    try:
        with lock:
-            with open(config_file) as f:
+            with open(config_file, "r") as f:
                old_raw_config = f.read()

            try:
@@ -831,13 +790,6 @@ def config_set(request: Request, body: AppConfigSetBody):
                    updates = flatten_config_data(body.config_data)
                    # Convert None values to empty strings for deletion (e.g., when deleting masks)
                    updates = {k: ("" if v is None else v) for k, v in updates.items()}
-                    # Drop sentinel-valued fields so untouched credential
-                    # placeholders don't clobber the saved YAML value.
-                    updates = {
-                        k: v
-                        for k, v in updates.items()
-                        if v != REDACTED_CREDENTIAL_SENTINEL
-                    }

                if not updates:
                    return JSONResponse(
@@ -854,7 +806,7 @@ def config_set(request: Request, body: AppConfigSetBody):
                update_yaml_file_bulk(config_file, updates)

                # validate the updated config
-                with open(config_file) as f:
+                with open(config_file, "r") as f:
                    new_raw_config = f.read()

                try:
@@ -908,11 +860,6 @@ def config_set(request: Request, body: AppConfigSetBody):
                    status_code=500,
                )

-            # drop runtime overrides for any fields the user just rewrote in
-            # yaml so a stale override doesn't silently win after restart
-            if request.app.dispatcher is not None:
-                request.app.dispatcher.clear_runtime_state_for_yaml_keys(updates.keys())
-
            if body.requires_restart == 0 or body.update_topic:
                old_config: FrigateConfig = request.app.frigate_config
                request.app.frigate_config = config
@@ -1028,16 +975,16 @@ def nvinfo():
 )
 async def logs(
    service: str = Path(enum=["frigate", "nginx", "go2rtc"]),
-    download: str | None = None,
-    stream: bool | None = False,
-    start: int | None = 0,
-    end: int | None = None,
+    download: Optional[str] = None,
+    stream: Optional[bool] = False,
+    start: Optional[int] = 0,
+    end: Optional[int] = None,
 ):
    """Get logs for the requested service (frigate/nginx/go2rtc)"""

    def download_logs(service_location: str):
        try:
-            file = open(service_location)
+            file = open(service_location, "r")
            contents = file.read()
            file.close()
            return JSONResponse(jsonable_encoder(contents))
@@ -1052,7 +999,7 @@ async def logs(
        """Asynchronously stream log lines."""
        buffer = ""
        try:
-            async with aiofiles.open(file_path) as file:
+            async with aiofiles.open(file_path, "r") as file:
                await file.seek(0, 2)
                while True:
                    line = await file.readline()
@@ -1090,7 +1037,7 @@ async def logs(

    # For full logs initially
    try:
-        async with aiofiles.open(service_location) as file:
+        async with aiofiles.open(service_location, "r") as file:
            contents = await file.read()

        total_lines, log_lines = process_logs(contents, service, start, end)
@@ -1231,7 +1178,7 @@ def get_media_sync_status(job_id: str):
@router.get("/labels", dependencies=[Depends(allow_any_authenticated())])
 def get_labels(
    camera: str = "",
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
    try:
        if camera:
@@ -1263,8 +1210,8 @@ def get_labels(

@router.get("/sub_labels", dependencies=[Depends(allow_any_authenticated())])
 def get_sub_labels(
-    split_joined: int | None = None,
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    split_joined: Optional[int] = None,
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
    try:
        events = (
@@ -1351,8 +1298,8 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
    "/recognized_license_plates", dependencies=[Depends(allow_any_authenticated())]
 )
 def get_recognized_license_plates(
-    split_joined: int | None = None,
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    split_joined: Optional[int] = None,
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
    try:
        query = (
@@ -1393,8 +1340,8 @@ def get_recognized_license_plates(
 def timeline(
    camera: str = "all",
    limit: int = 100,
-    source_id: str | None = None,
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    source_id: Optional[str] = None,
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
    clauses = []

@@ -1408,20 +1355,20 @@ def timeline(
    ]

    if camera != "all":
-        clauses.append(Timeline.camera == camera)
+        clauses.append((Timeline.camera == camera))

    if source_id:
        source_ids = [sid.strip() for sid in source_id.split(",")]
        if len(source_ids) == 1:
-            clauses.append(Timeline.source_id == source_ids[0])
+            clauses.append((Timeline.source_id == source_ids[0]))
        else:
-            clauses.append(Timeline.source_id.in_(source_ids))
+            clauses.append((Timeline.source_id.in_(source_ids)))

    # Enforce per-camera access control
-    clauses.append(Timeline.camera << allowed_cameras)
+    clauses.append((Timeline.camera << allowed_cameras))

    if len(clauses) == 0:
-        clauses.append(True)
+        clauses.append((True))

    timeline = (
        Timeline.select(*selected_columns)
@@ -1437,7 +1384,7 @@ def timeline(
@router.get("/timeline/hourly", dependencies=[Depends(allow_any_authenticated())])
 def hourly_timeline(
    params: AppTimelineHourlyQueryParameters = Depends(),
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
    """Get hourly summary for timeline."""
    cameras = params.cameras
@@ -1454,23 +1401,23 @@ def hourly_timeline(

    if cameras != "all":
        camera_list = cameras.split(",")
-        clauses.append(Timeline.camera << camera_list)
+        clauses.append((Timeline.camera << camera_list))

    # Enforce per-camera access control
-    clauses.append(Timeline.camera << allowed_cameras)
+    clauses.append((Timeline.camera << allowed_cameras))

    if labels != "all":
        label_list = labels.split(",")
-        clauses.append(Timeline.data["label"] << label_list)
+        clauses.append((Timeline.data["label"] << label_list))

    if before:
-        clauses.append(Timeline.timestamp < before)
+        clauses.append((Timeline.timestamp < before))

    if after:
-        clauses.append(Timeline.timestamp > after)
+        clauses.append((Timeline.timestamp > after))

    if len(clauses) == 0:
-        clauses.append(True)
+        clauses.append((True))

    timeline = (
        Timeline.select(
@@ -11,7 +11,7 @@ import secrets
 import time
 from datetime import datetime
 from pathlib import Path
-from urllib.parse import parse_qs, urlparse
+from typing import List, Optional

 from fastapi import APIRouter, Depends, HTTPException, Request, Response
 from fastapi.responses import JSONResponse, RedirectResponse
@@ -26,11 +26,7 @@ from frigate.api.defs.request.app_body import (
    AppPutRoleBody,
 )
 from frigate.api.defs.tags import Tags
-from frigate.api.media_auth import (
-    check_camera_access,
-    deny_response_for_media_uri,
-    is_role_restricted,
-)
+from frigate.api.media_auth import check_camera_access, deny_response_for_media_uri
 from frigate.config import AuthConfig, NetworkingConfig, ProxyConfig
 from frigate.const import CONFIG_DIR, JWT_SECRET_ENV_VAR, PASSWORD_HASH_ALGORITHM
 from frigate.models import User
@@ -253,14 +249,7 @@ rateLimiter = RateLimiter()


 def get_remote_addr(request: Request):
-    # fall back to the direct TCP peer when no proxy chain is present
-    direct_addr = request.client.host if request.client else None
-
-    forwarded_for = request.headers.get("x-forwarded-for")
-    if not forwarded_for:
-        return direct_addr or "127.0.0.1"
-
-    route = list(reversed(forwarded_for.split(",")))
+    route = list(reversed(request.headers.get("x-forwarded-for").split(",")))
    logger.debug(f"IP Route: {[r for r in route]}")
    trusted_proxies = []
    for proxy in request.app.frigate_config.auth.trusted_proxies:
@@ -297,8 +286,13 @@ def get_remote_addr(request: Request):
            logger.debug(f"First untrusted IP: {str(ip)}")
            return str(ip)

-    # every hop in the route was trusted, so fall back to the direct peer
-    return direct_addr or "127.0.0.1"
+    # if there wasn't anything in the route, just return the default
+    remote_addr = None
+
+    if hasattr(request, "remote_addr"):
+        remote_addr = request.remote_addr
+
+    return remote_addr or "127.0.0.1"


 def _cleanup_first_load_seen() -> None:
@@ -389,7 +383,7 @@ def verify_password(password, password_hash):
    return secrets.compare_digest(password_hash, compare_hash)


-def validate_password_strength(password: str) -> tuple[bool, str | None]:
+def validate_password_strength(password: str) -> tuple[bool, Optional[str]]:
    """
    Validate password strength.

@@ -417,12 +411,6 @@ def create_encoded_jwt(user, role, expiration, secret):

 def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, expiration, secure):
    # TODO: ideally this would set secure as well, but that requires TLS
-    # SameSite is intentionally left unset (browsers default to Lax). Setting
-    # SameSite=Lax/Strict would stop the cookie from being sent in cross-origin
-    # iframes, breaking embedded views such as the Home Assistant Frigate card.
-    # CSRF is instead mitigated by requiring a custom X-CSRF-TOKEN header, which
-    # cross-origin pages cannot set without a CORS preflight that Frigate never
-    # grants (see check_csrf in api/fastapi_app.py).
    response.set_cookie(
        key=cookie_name,
        value=encoded_jwt,
@@ -444,7 +432,7 @@ async def get_current_user(request: Request):
    return {"username": username, "role": role}


-def require_role(required_roles: list[str]):
+def require_role(required_roles: List[str]):
    async def role_checker(request: Request):
        proxy_config: ProxyConfig = request.app.frigate_config.proxy
        config_roles = list(request.app.frigate_config.auth.roles.keys())
@@ -670,10 +658,6 @@ def auth(request: Request):
        if deny_status is not None:
            return Response("", status_code=deny_status)

-        deny_status = deny_response_for_go2rtc_stream(original_url, role, request)
-        if deny_status is not None:
-            return Response("", status_code=deny_status)
-
        return success_response

    # now apply authentication
@@ -773,10 +757,6 @@ def auth(request: Request):
        if deny_status is not None:
            return Response("", status_code=deny_status)

-        deny_status = deny_response_for_go2rtc_stream(original_url, role, request)
-        if deny_status is not None:
-            return Response("", status_code=deny_status)
-
        return success_response
    except Exception as e:
        logger.error(f"Error parsing jwt: {e}")
@@ -1082,7 +1062,7 @@ async def update_role(


 async def require_camera_access(
-    camera_name: str | None = None,
+    camera_name: Optional[str] = None,
    request: Request = None,
 ):
    """Dependency to enforce camera access based on user role."""
@@ -1132,68 +1112,8 @@ def _get_stream_owner_cameras(request: Request, stream_name: str) -> set[str]:
    return owner_cameras


-# nginx proxies these paths straight to go2rtc with authentication-only checks
-# (see auth_request.conf). Each names the desired stream via the `src` query
-# param, so the camera-level check must happen here in the `/auth` subrequest —
-# `require_go2rtc_stream_access` only guards the REST `/go2rtc/streams/{name}`
-# endpoint, not these proxied live-stream paths.
-GO2RTC_STREAM_PROXY_PATHS = frozenset(
-    {
-        "/live/mse/api/ws",
-        "/live/webrtc/api/ws",
-        "/api/go2rtc/webrtc",
-    }
-)
-
-
-def deny_response_for_go2rtc_stream(
-    original_url: str | None, role: str | None, request: Request
-) -> int | None:
-    """Block role-restricted users from go2rtc live streams they cannot access.
-
-    Returns 403 when any `src` stream named in `original_url` resolves to a
-    camera outside the role's allow-list (or when no `src` is provided on a
-    stream-proxy path), otherwise None. Mirrors the resolution logic in
-    `require_go2rtc_stream_access` so substream names map to their owning
-    camera correctly.
-    """
-    if not original_url:
-        return None
-
-    parsed = urlparse(original_url)
-    if parsed.path not in GO2RTC_STREAM_PROXY_PATHS:
-        return None
-
-    frigate_config = request.app.frigate_config
-
-    # admin and full-access roles (no allow-list) bypass the camera check
-    if not role or not is_role_restricted(role, frigate_config):
-        return None
-
-    sources = parse_qs(parsed.query).get("src", [])
-    if not sources:
-        # a stream-proxy request naming no stream has nothing legitimate to
-        # show a restricted user
-        return 403
-
-    allowed_cameras = set(
-        User.get_allowed_cameras(
-            role,
-            frigate_config.auth.roles,
-            set(frigate_config.cameras.keys()),
-        )
-    )
-
-    # deny if any requested source resolves outside the allow-list
-    for src in sources:
-        if not (_get_stream_owner_cameras(request, src) & allowed_cameras):
-            return 403
-
-    return None
-
-
 async def require_go2rtc_stream_access(
-    stream_name: str | None = None,
+    stream_name: Optional[str] = None,
    request: Request = None,
 ):
    """Dependency to enforce go2rtc stream access based on owning camera access."""
@@ -34,15 +34,11 @@ from frigate.config.camera.updater import (
 )
 from frigate.config.env import substitute_frigate_vars
 from frigate.models import User
-from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
+from frigate.util.builtin import clean_camera_user_pass
 from frigate.util.camera_cleanup import cleanup_camera_db, cleanup_camera_files
 from frigate.util.config import find_config_file
 from frigate.util.image import run_ffmpeg_snapshot
-from frigate.util.services import (
-    analyze_record_keyframes,
-    ffprobe_stream,
-    is_restricted_go2rtc_source,
-)
+from frigate.util.services import ffprobe_stream, is_restricted_go2rtc_source

 logger = logging.getLogger(__name__)

@@ -74,7 +70,7 @@ def _is_valid_host(host: str) -> bool:

@router.get("/go2rtc/streams", dependencies=[Depends(allow_any_authenticated())])
 async def go2rtc_streams(request: Request):
-    r = await asyncio.to_thread(requests.get, "http://127.0.0.1:1984/api/streams")
+    r = requests.get("http://127.0.0.1:1984/api/streams")
    if not r.ok:
        logger.error("Failed to fetch streams from go2rtc")
        return JSONResponse(
@@ -147,19 +143,6 @@ def go2rtc_camera_stream(request: Request, stream_name: str):
 )
 def go2rtc_add_stream(request: Request, stream_name: str, src: str = ""):
    """Add or update a go2rtc stream configuration."""
-    if src and is_restricted_go2rtc_source(src):
-        logger.warning(
-            "Rejected go2rtc stream '%s' with restricted source type (echo/expr/exec)",
-            stream_name,
-        )
-        return JSONResponse(
-            content={
-                "success": False,
-                "message": "Restricted stream source type",
-            },
-            status_code=400,
-        )
-
    try:
        params = {"name": stream_name}
        if src:
@@ -379,48 +362,6 @@ def ffprobe(request: Request, paths: str = "", detailed: bool = False):
    return JSONResponse(content=output)


-@router.get("/keyframe_analysis", dependencies=[Depends(require_role(["admin"]))])
-async def keyframe_analysis(request: Request, camera: str = ""):
-    """Probe a camera's record stream and classify its keyframe spacing.
-
-    Detects smart/+ codecs and long/variable GOPs that degrade recording.
-    """
-    config: FrigateConfig = request.app.frigate_config
-
-    if camera not in config.cameras:
-        return JSONResponse(
-            content={"success": False, "message": f"{camera} is not a valid camera."},
-            status_code=404,
-        )
-
-    camera_config = config.cameras[camera]
-
-    if not camera_config.enabled:
-        return JSONResponse(
-            content={"success": False, "message": f"{camera} is not enabled."},
-            status_code=404,
-        )
-
-    # keyframe spacing only matters when this camera is recording
-    if not camera_config.record.enabled:
-        return JSONResponse(content={"severity": "record_disabled"})
-
-    # recording guarantees an input carries the record role; its index matches
-    # the "Stream N" numbering the ffprobe endpoint surfaces (same input order)
-    record_index, record_input = next(
-        (idx, i)
-        for idx, i in enumerate(camera_config.ffmpeg.inputs)
-        if "record" in i.roles
-    )
-
-    segment_time = get_record_segment_time(camera_config)
-    result = await analyze_record_keyframes(
-        config.ffmpeg, record_input.path, segment_time
-    )
-    result["stream_index"] = record_index
-    return JSONResponse(content=result)
-
-
@router.get("/ffprobe/snapshot", dependencies=[Depends(require_role(["admin"]))])
 def ffprobe_snapshot(request: Request, url: str = "", timeout: int = 10):
    """Get a snapshot from a stream URL using ffmpeg."""
@@ -588,68 +529,6 @@ def _extract_fps(r_frame_rate: str) -> float | None:
        return None


-def _build_digest_transport(username: str, password: str) -> AsyncTransport:
-    """Build a zeep transport backed by an httpx client using HTTP digest auth."""
-    auth = httpx.DigestAuth(username, password)
-    client = httpx.AsyncClient(auth=auth, timeout=10.0)
-    return AsyncTransport(client=client)
-
-
-async def _connect_onvif_camera(
-    host: str,
-    port: int,
-    username: str,
-    password: str,
-    wsdl_base: str | None,
-    auth_type: str,
-) -> ONVIFCamera:
-    """Connect to an ONVIF device, trying both WS-Security password encodings.
-
-    Cameras disagree on whether the WS-Security UsernameToken should carry a
-    hashed PasswordDigest or a plaintext PasswordText. The wizard can't know
-    which a given camera expects, so we try PasswordDigest first (the common
-    case) and fall back to PasswordText when the device rejects the token. This
-    is independent of auth_type, which controls HTTP transport-level auth.
-    """
-    first_error: Fault | None = None
-
-    # encrypt=True -> PasswordDigest, encrypt=False -> PasswordText
-    for encrypt in (True, False):
-        onvif_camera = ONVIFCamera(
-            host,
-            port,
-            username or "",
-            password or "",
-            wsdl_dir=wsdl_base,
-            encrypt=encrypt,
-        )
-
-        try:
-            await onvif_camera.update_xaddrs()
-        except Fault as e:
-            # A SOAP fault here is how a camera signals the wrong password
-            # encoding, so retry with the other encoding before giving up.
-            logger.debug(
-                "ONVIF connect with %s rejected, trying alternate encoding",
-                "PasswordDigest" if encrypt else "PasswordText",
-            )
-            if first_error is None:
-                first_error = e
-            continue
-
-        if auth_type == "digest" and username and password:
-            transport = _build_digest_transport(username, password)
-            for service in ("devicemgmt", "media", "ptz"):
-                if hasattr(onvif_camera, service):
-                    getattr(onvif_camera, service).zeep_client.transport = transport
-            logger.debug("Configured digest authentication")
-
-        return onvif_camera
-
-    # Both encodings failed authentication; surface the original fault.
-    raise first_error
-
-
@router.get(
    "/onvif/probe",
    dependencies=[Depends(require_role(["admin"]))],
@@ -726,10 +605,34 @@ async def onvif_probe(
        except Exception:
            wsdl_base = None

-        onvif_camera = await _connect_onvif_camera(
-            host, port, username, password, wsdl_base, auth_type
+        onvif_camera = ONVIFCamera(
+            host, port, username or "", password or "", wsdl_dir=wsdl_base
        )

+        # Configure digest authentication if requested
+        if auth_type == "digest" and username and password:
+            # Create httpx client with digest auth
+            auth = httpx.DigestAuth(username, password)
+            client = httpx.AsyncClient(auth=auth, timeout=10.0)
+
+            # Replace the transport in the zeep client
+            transport = AsyncTransport(client=client)
+
+            # Update the xaddr before setting transport
+            await onvif_camera.update_xaddrs()
+
+            # Replace transport in all services
+            if hasattr(onvif_camera, "devicemgmt"):
+                onvif_camera.devicemgmt.zeep_client.transport = transport
+            if hasattr(onvif_camera, "media"):
+                onvif_camera.media.zeep_client.transport = transport
+            if hasattr(onvif_camera, "ptz"):
+                onvif_camera.ptz.zeep_client.transport = transport
+
+            logger.debug("Configured digest authentication")
+        else:
+            await onvif_camera.update_xaddrs()
+
        # Get device information
        device_info = {
            "manufacturer": "Unknown",
@@ -741,9 +644,10 @@ async def onvif_probe(

            # Update transport for device service if digest auth
            if auth_type == "digest" and username and password:
-                device_service.zeep_client.transport = _build_digest_transport(
-                    username, password
-                )
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                device_service.zeep_client.transport = transport

            device_info_resp = await device_service.GetDeviceInformation()
            manufacturer = getattr(device_info_resp, "Manufacturer", None) or (
@@ -781,9 +685,10 @@ async def onvif_probe(

            # Update transport for media service if digest auth
            if auth_type == "digest" and username and password:
-                media_service.zeep_client.transport = _build_digest_transport(
-                    username, password
-                )
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                media_service.zeep_client.transport = transport

            profiles = await media_service.GetProfiles()
            profiles_count = len(profiles) if profiles else 0
@@ -815,9 +720,10 @@ async def onvif_probe(

            # Update transport for PTZ service if digest auth
            if auth_type == "digest" and username and password:
-                ptz_service.zeep_client.transport = _build_digest_transport(
-                    username, password
-                )
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                ptz_service.zeep_client.transport = transport

            # Check if PTZ service is available
            try:
@@ -970,9 +876,10 @@ async def onvif_probe(

            # Update transport for media service if digest auth
            if auth_type == "digest" and username and password:
-                media_service.zeep_client.transport = _build_digest_transport(
-                    username, password
-                )
+                auth = httpx.DigestAuth(username, password)
+                client = httpx.AsyncClient(auth=auth, timeout=10.0)
+                transport = AsyncTransport(client=client)
+                media_service.zeep_client.transport = transport

            if profiles_count and media_service:
                for p in profiles or []:
@@ -1187,14 +1094,14 @@ async def delete_camera(

    try:
        with lock:
-            with open(config_file) as f:
+            with open(config_file, "r") as f:
                old_raw_config = f.read()

            try:
                yaml = YAML()
                yaml.indent(mapping=2, sequence=4, offset=2)

-                with open(config_file) as f:
+                with open(config_file, "r") as f:
                    data = yaml.load(f)

                # Remove camera from config
@@ -1223,7 +1130,7 @@ async def delete_camera(
                with open(config_file, "w") as f:
                    yaml.dump(data, f)

-                with open(config_file) as f:
+                with open(config_file, "r") as f:
                    new_raw_config = f.read()

                try:
@@ -1285,8 +1192,7 @@ async def delete_camera(

    # Best-effort go2rtc stream removal
    try:
-        await asyncio.to_thread(
-            requests.delete,
+        requests.delete(
            "http://127.0.0.1:1984/api/streams",
            params={"src": camera_name},
            timeout=5,
@@ -7,7 +7,7 @@ import operator
 import time
 from datetime import datetime
 from functools import reduce
-from typing import Any
+from typing import Any, Dict, List, Optional

 import cv2
 from fastapi import APIRouter, Body, Depends, HTTPException, Request
@@ -59,7 +59,7 @@ class ToolExecuteRequest(BaseModel):
    """Request model for tool execution."""

    tool_name: str
-    arguments: dict[str, Any]
+    arguments: Dict[str, Any]


 class VLMMonitorRequest(BaseModel):
@@ -68,8 +68,8 @@ class VLMMonitorRequest(BaseModel):
    camera: str
    condition: str
    max_duration_minutes: int = 60
-    labels: list[str] = []
-    zones: list[str] = []
+    labels: List[str] = []
+    zones: List[str] = []


@router.get(
@@ -91,10 +91,10 @@ def get_tools(request: Request) -> JSONResponse:


 def _resolve_zones(
-    zones: list[str],
+    zones: List[str],
    config: FrigateConfig,
-    target_cameras: list[str],
-) -> list[str]:
+    target_cameras: List[str],
+) -> List[str]:
    """Map zone names to their canonical config keys, case-insensitively.

    LLMs frequently echo a user's casing ("Front Yard") instead of the
@@ -107,7 +107,7 @@ def _resolve_zones(
    if not zones:
        return zones

-    lookup: dict[str, str] = {}
+    lookup: Dict[str, str] = {}
    for camera_id in target_cameras:
        camera_config = config.cameras.get(camera_id)
        if camera_config is None:
@@ -120,8 +120,8 @@ def _resolve_zones(

 async def _execute_search_objects(
    request: Request,
-    arguments: dict[str, Any],
-    allowed_cameras: list[str],
+    arguments: Dict[str, Any],
+    allowed_cameras: List[str],
 ) -> JSONResponse:
    """
    Execute the search_objects tool.
@@ -201,7 +201,7 @@ async def _execute_search_objects(
        # Return it as-is for the LLM
        return response
    except Exception as e:
-        logger.exception(f"Error executing search_objects: {e}")
+        logger.error(f"Error executing search_objects: {e}", exc_info=True)
        return JSONResponse(
            content={
                "success": False,
@@ -213,8 +213,8 @@ async def _execute_search_objects(

 async def _execute_search_objects_semantic(
    request: Request,
-    arguments: dict[str, Any],
-    allowed_cameras: list[str],
+    arguments: Dict[str, Any],
+    allowed_cameras: List[str],
    semantic_query: str,
 ) -> JSONResponse:
    """Search objects via fused thumbnail + description embeddings.
@@ -263,8 +263,8 @@ async def _execute_search_objects_semantic(
    limit = int(arguments.get("limit", 25))
    limit = max(1, min(limit, 100))

-    visual_distances: dict[str, float] = {}
-    description_distances: dict[str, float] = {}
+    visual_distances: Dict[str, float] = {}
+    description_distances: Dict[str, float] = {}
    try:
        rows = context.search_thumbnail(semantic_query)
        visual_distances = {row[0]: row[1] for row in rows}
@@ -305,7 +305,7 @@ async def _execute_search_objects_semantic(

    eligible = {e.id: e for e in Event.select().where(reduce(operator.and_, clauses))}

-    scored: list[tuple[str, float]] = []
+    scored: List[tuple[str, float]] = []
    for eid in eligible:
        v_score = (
            distance_to_score(visual_distances[eid], context.thumb_stats)
@@ -331,9 +331,9 @@ async def _execute_search_objects_semantic(

 async def _execute_find_similar_objects(
    request: Request,
-    arguments: dict[str, Any],
-    allowed_cameras: list[str],
-) -> dict[str, Any]:
+    arguments: Dict[str, Any],
+    allowed_cameras: List[str],
+) -> Dict[str, Any]:
    """Execute the find_similar_objects tool.

    Returns a plain dict (not JSONResponse) so the chat loop can embed it
@@ -403,8 +403,8 @@ async def _execute_find_similar_objects(
    # version (see frigate/embeddings/__init__.py). Mirror the pattern used by
    # frigate/api/event.py events_search: fetch top-k globally, then intersect
    # with the structured filters via Peewee.
-    visual_distances: dict[str, float] = {}
-    description_distances: dict[str, float] = {}
+    visual_distances: Dict[str, float] = {}
+    description_distances: Dict[str, float] = {}

    try:
        if similarity_mode in ("visual", "fused"):
@@ -462,7 +462,7 @@ async def _execute_find_similar_objects(
    eligible = {e.id: e for e in Event.select().where(reduce(operator.and_, clauses))}

    # 6. Fuse and rank.
-    scored: list[tuple[str, float]] = []
+    scored: List[tuple[str, float]] = []
    for eid in eligible:
        v_score = (
            distance_to_score(visual_distances[eid], context.thumb_stats)
@@ -503,7 +503,7 @@ async def _execute_find_similar_objects(
 async def execute_tool(
    request: Request,
    body: ToolExecuteRequest = Body(...),
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ) -> JSONResponse:
    """
    Execute a tool function call.
@@ -545,23 +545,11 @@ async def execute_tool(
 async def _execute_get_live_context(
    request: Request,
    camera: str,
-    allowed_cameras: list[str],
-) -> dict[str, Any]:
-    # Reject wildcards explicitly so models retry with a real camera name
-    # instead of silently fanning out across every camera.
-    if camera in ("*", "all"):
-        return {
-            "error": (
-                "get_live_context requires a single camera name; wildcards "
-                "are not supported. Call this tool once per camera."
-            ),
-            "available_cameras": allowed_cameras,
-        }
-
+    allowed_cameras: List[str],
+) -> Dict[str, Any]:
    if camera not in allowed_cameras:
        return {
            "error": f"Camera '{camera}' not found or access denied",
-            "available_cameras": allowed_cameras,
        }

    if camera not in request.app.frigate_config.cameras:
@@ -593,7 +581,7 @@ async def _execute_get_live_context(
                    "stationary": obj_dict.get("stationary", False),
                }

-        result: dict[str, Any] = {
+        result: Dict[str, Any] = {
            "camera": camera,
            "timestamp": frame_time,
            "detections": list(tracked_objects_dict.values()),
@@ -611,7 +599,7 @@ async def _execute_get_live_context(
        return result

    except Exception as e:
-        logger.exception(f"Error executing get_live_context: {e}")
+        logger.error(f"Error executing get_live_context: {e}", exc_info=True)
        return {
            "error": "Error getting live context",
        }
@@ -620,8 +608,8 @@ async def _execute_get_live_context(
 async def _get_live_frame_image_url(
    request: Request,
    camera: str,
-    allowed_cameras: list[str],
-) -> str | None:
+    allowed_cameras: List[str],
+) -> Optional[str]:
    """
    Fetch the current live frame for a camera as a base64 data URL.

@@ -659,8 +647,8 @@ async def _get_live_frame_image_url(

 async def _execute_set_camera_state(
    request: Request,
-    arguments: dict[str, Any],
-) -> dict[str, Any]:
+    arguments: Dict[str, Any],
+) -> Dict[str, Any]:
    role = request.headers.get("remote-role", "")
    if "admin" not in [r.strip() for r in role.split(",")]:
        return {"error": "Admin privileges required to change camera settings."}
@@ -699,10 +687,10 @@ async def _execute_set_camera_state(

 async def _execute_tool_internal(
    tool_name: str,
-    arguments: dict[str, Any],
+    arguments: Dict[str, Any],
    request: Request,
-    allowed_cameras: list[str],
-) -> dict[str, Any]:
+    allowed_cameras: List[str],
+) -> Dict[str, Any]:
    """
    Internal helper to execute a tool and return the result as a dict.

@@ -733,14 +721,7 @@ async def _execute_tool_internal(
                "Arguments: %s",
                json.dumps(arguments),
            )
-            return {
-                "error": (
-                    "get_live_context requires a single camera name; "
-                    "wildcards and empty values are not supported. "
-                    "Call this tool once per camera."
-                ),
-                "available_cameras": allowed_cameras,
-            }
+            return {"error": "Camera parameter is required"}
        return await _execute_get_live_context(request, camera, allowed_cameras)
    elif tool_name == "start_camera_watch":
        return await _execute_start_camera_watch(request, arguments)
@@ -763,8 +744,8 @@ async def _execute_tool_internal(

 async def _execute_start_camera_watch(
    request: Request,
-    arguments: dict[str, Any],
-) -> dict[str, Any]:
+    arguments: Dict[str, Any],
+) -> Dict[str, Any]:
    camera = arguments.get("camera", "").strip()
    condition = arguments.get("condition", "").strip()
    max_duration_minutes = int(arguments.get("max_duration_minutes", 60))
@@ -801,7 +782,7 @@ async def _execute_start_camera_watch(
            zones=zones,
        )
    except RuntimeError as e:
-        logger.exception("Failed to start VLM watch job: %s", e)
+        logger.error("Failed to start VLM watch job: %s", e, exc_info=True)
        return {"error": "Failed to start VLM watch job."}

    return {
@@ -814,14 +795,14 @@ async def _execute_start_camera_watch(
    }


-def _execute_stop_camera_watch() -> dict[str, Any]:
+def _execute_stop_camera_watch() -> Dict[str, Any]:
    cancelled = stop_vlm_watch_job()
    if cancelled:
        return {"success": True, "message": "Watch job cancelled."}
    return {"success": False, "message": "No active watch job to cancel."}


-def _execute_get_profile_status(request: Request) -> dict[str, Any]:
+def _execute_get_profile_status(request: Request) -> Dict[str, Any]:
    """Return profile status including active profile and activation timestamps."""
    profile_manager = getattr(request.app, "profile_manager", None)
    if profile_manager is None:
@@ -846,9 +827,9 @@ def _execute_get_profile_status(request: Request) -> dict[str, Any]:


 def _execute_get_recap(
-    arguments: dict[str, Any],
-    allowed_cameras: list[str],
-) -> dict[str, Any]:
+    arguments: Dict[str, Any],
+    allowed_cameras: List[str],
+) -> Dict[str, Any]:
    """Fetch review segments with GenAI metadata for a time period."""
    from functools import reduce

@@ -909,7 +890,7 @@ def _execute_get_recap(
            .iterator()
        )

-        events: list[dict[str, Any]] = []
+        events: List[Dict[str, Any]] = []

        for row in rows:
            data = row.get("data") or {}
@@ -920,7 +901,7 @@ def _execute_get_recap(
                    data = {}

            camera = row["camera"]
-            event: dict[str, Any] = {
+            event: Dict[str, Any] = {
                "camera": camera.replace("_", " ").title(),
                "severity": row.get("severity", "detection"),
            }
@@ -979,15 +960,15 @@ def _execute_get_recap(

        return {"events": events}
    except Exception as e:
-        logger.exception("Error executing get_recap: %s", e)
+        logger.error("Error executing get_recap: %s", e, exc_info=True)
        return {"error": "Failed to fetch recap data."}


 async def _execute_pending_tools(
-    pending_tool_calls: list[dict[str, Any]],
+    pending_tool_calls: List[Dict[str, Any]],
    request: Request,
-    allowed_cameras: list[str],
-) -> tuple[list[ToolCall], list[dict[str, Any]], list[dict[str, Any]]]:
+    allowed_cameras: List[str],
+) -> tuple[List[ToolCall], List[Dict[str, Any]], List[Dict[str, Any]]]:
    """
    Execute a list of tool calls.

@@ -996,9 +977,9 @@ async def _execute_pending_tools(
         tool result dicts for conversation,
         extra messages to inject after tool results — e.g. user messages with images)
    """
-    tool_calls_out: list[ToolCall] = []
-    tool_results: list[dict[str, Any]] = []
-    extra_messages: list[dict[str, Any]] = []
+    tool_calls_out: List[ToolCall] = []
+    tool_results: List[Dict[str, Any]] = []
+    extra_messages: List[Dict[str, Any]] = []
    for tool_call in pending_tool_calls:
        tool_name = tool_call["name"]
        tool_args = tool_call.get("arguments") or {}
@@ -1072,12 +1053,13 @@ async def _execute_pending_tools(
                }
            )
        except Exception as e:
-            logger.exception(
+            logger.error(
                "Error executing tool %s (id: %s): %s. Arguments: %s",
                tool_name,
                tool_call_id,
                e,
                json.dumps(tool_args),
+                exc_info=True,
            )
            error_content = json.dumps({"error": f"Tool execution failed: {str(e)}"})
            tool_calls_out.append(
@@ -1105,7 +1087,7 @@ async def _execute_pending_tools(
 async def chat_completion(
    request: Request,
    body: ChatCompletionRequest = Body(...),
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
 ):
    """
    Chat completion endpoint with tool calling support.
@@ -1137,23 +1119,19 @@ async def chat_completion(
    )
    conversation = []

-    # Build the system message only when the client hasn't already pinned one.
-    # The first turn has no system message; we generate it (with the current
-    # timestamp) and return the whole chain so the client persists it. Later
-    # turns send it back verbatim, freezing the timestamp so the prompt prefix
-    # stays byte-identical and the model server's prompt cache keeps hitting.
-    if not body.messages or body.messages[0].role != "system":
-        conversation.append(
-            {
-                "role": "system",
-                "content": build_chat_system_prompt(
-                    config=config,
-                    allowed_cameras=allowed_cameras,
-                    semantic_search_enabled=semantic_search_enabled,
-                    attribute_classifications=attribute_classifications,
-                ),
-            }
-        )
+    system_prompt = build_chat_system_prompt(
+        config=config,
+        allowed_cameras=allowed_cameras,
+        semantic_search_enabled=semantic_search_enabled,
+        attribute_classifications=attribute_classifications,
+    )
+
+    conversation.append(
+        {
+            "role": "system",
+            "content": system_prompt,
+        }
+    )

    for msg in body.messages:
        msg_dict = {
@@ -1164,13 +1142,11 @@ async def chat_completion(
            msg_dict["tool_call_id"] = msg.tool_call_id
        if msg.name:
            msg_dict["name"] = msg.name
-        if msg.tool_calls is not None:
-            msg_dict["tool_calls"] = msg.tool_calls

        conversation.append(msg_dict)

    tool_iterations = 0
-    tool_calls: list[ToolCall] = []
+    tool_calls: List[ToolCall] = []
    max_iterations = body.max_tool_iterations

    logger.debug(
@@ -1180,20 +1156,11 @@ async def chat_completion(

    # True LLM streaming when client supports it and stream requested
    if body.stream and hasattr(genai_client, "chat_with_tools_stream"):
+        stream_tool_calls: List[ToolCall] = []
        stream_iterations = 0

        async def stream_body_llm():
-            nonlocal conversation, stream_iterations
-
-            def _emit_chain(extra: list[dict[str, Any]] | None = None):
-                # Return the full conversation (including the system message) so
-                # the client persists and replays it verbatim next turn.
-                chain = conversation + (extra or [])
-                return (
-                    json.dumps({"type": "messages", "messages": chain}).encode("utf-8")
-                    + b"\n"
-                )
-
+            nonlocal conversation, stream_tool_calls, stream_iterations
            while stream_iterations < max_iterations:
                if await request.is_disconnected():
                    logger.debug("Client disconnected, stopping chat stream")
@@ -1206,7 +1173,6 @@ async def chat_completion(
                    messages=conversation,
                    tools=tools if tools else None,
                    tool_choice="auto",
-                    enable_thinking=body.enable_thinking,
                ):
                    if await request.is_disconnected():
                        logger.debug("Client disconnected, stopping chat stream")
@@ -1258,33 +1224,31 @@ async def chat_completion(
                                )
                                return
                            (
-                                _executed_calls,
+                                executed_calls,
                                tool_results,
                                extra_msgs,
                            ) = await _execute_pending_tools(
                                pending, request, allowed_cameras
                            )
+                            stream_tool_calls.extend(executed_calls)
                            conversation.extend(tool_results)
                            conversation.extend(extra_msgs)
-                            # Emit the running chain so the client can render tool
-                            # calls live and replay them verbatim next turn.
-                            yield _emit_chain()
+                            yield (
+                                json.dumps(
+                                    {
+                                        "type": "tool_calls",
+                                        "tool_calls": [
+                                            tc.model_dump() for tc in stream_tool_calls
+                                        ],
+                                    }
+                                ).encode("utf-8")
+                                + b"\n"
+                            )
                            break
                        else:
-                            # Streaming never appends the final assistant message
-                            # to the conversation, so add it to the chain.
-                            yield _emit_chain(
-                                extra=[
-                                    {
-                                        "role": "assistant",
-                                        "content": msg.get("content"),
-                                    }
-                                ]
-                            )
                            yield (json.dumps({"type": "done"}).encode("utf-8") + b"\n")
                            return
            else:
-                yield _emit_chain()
                yield json.dumps({"type": "done"}).encode("utf-8") + b"\n"

        return StreamingResponse(
@@ -1303,7 +1267,6 @@ async def chat_completion(
                messages=conversation,
                tools=tools if tools else None,
                tool_choice="auto",
-                enable_thinking=body.enable_thinking,
            )

            if response.get("finish_reason") == "error":
@@ -1331,15 +1294,19 @@ async def chat_completion(
                if body.stream:
                    final_reasoning = response.get("reasoning")

-                    chain = list(conversation)
-
                    async def stream_body() -> Any:
-                        yield (
-                            json.dumps({"type": "messages", "messages": chain}).encode(
-                                "utf-8"
+                        if tool_calls:
+                            yield (
+                                json.dumps(
+                                    {
+                                        "type": "tool_calls",
+                                        "tool_calls": [
+                                            tc.model_dump() for tc in tool_calls
+                                        ],
+                                    }
+                                ).encode("utf-8")
+                                + b"\n"
                            )
-                            + b"\n"
-                        )
                        # Emit the full reasoning trace up front when the
                        # underlying client did not stream it
                        if final_reasoning:
@@ -1375,7 +1342,6 @@ async def chat_completion(
                        finish_reason=response.get("finish_reason", "stop"),
                        tool_iterations=tool_iterations,
                        tool_calls=tool_calls,
-                        messages=list(conversation),
                    ).model_dump(),
                )

@@ -1408,12 +1374,11 @@ async def chat_completion(
                finish_reason="length",
                tool_iterations=tool_iterations,
                tool_calls=tool_calls,
-                messages=list(conversation),
            ).model_dump(),
        )

    except Exception as e:
-        logger.exception(f"Error in chat completion: {e}")
+        logger.error(f"Error in chat completion: {e}", exc_info=True)
        return JSONResponse(
            content={
                "error": "An error occurred while processing your request.",
@@ -1476,7 +1441,7 @@ async def start_vlm_monitor(
            username=request.headers.get("remote-user", ""),
        )
    except RuntimeError as e:
-        logger.exception("Failed to start VLM watch job: %s", e)
+        logger.error("Failed to start VLM watch job: %s", e, exc_info=True)
        return JSONResponse(
            content={"success": False, "message": "Failed to start VLM watch job."},
            status_code=409,
@@ -9,9 +9,8 @@ loop state — all inputs and outputs are plain data.
 import logging
 import math
 import time
-from collections.abc import Generator
 from datetime import datetime
-from typing import Any
+from typing import Any, Dict, Generator, List, Optional

 from frigate.embeddings.util import ZScoreNormalization
 from frigate.models import Event
@@ -31,7 +30,7 @@ def chunk_content(content: str, chunk_size: int = 80) -> Generator[str, None, No
    if not content:
        return
    words = content.split(" ")
-    current: list[str] = []
+    current: List[str] = []
    current_len = 0
    for w in words:
        current.append(w)
@@ -45,8 +44,8 @@ def chunk_content(content: str, chunk_size: int = 80) -> Generator[str, None, No


 def format_events_with_local_time(
-    events_list: list[dict[str, Any]],
-) -> list[dict[str, Any]]:
+    events_list: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
    """Add human-readable local start/end times to each event for the LLM."""
    result = []
    for evt in events_list:
@@ -85,9 +84,9 @@ def distance_to_score(distance: float, stats: ZScoreNormalization) -> float:


 def fuse_scores(
-    visual_score: float | None,
-    description_score: float | None,
-) -> float | None:
+    visual_score: Optional[float],
+    description_score: Optional[float],
+) -> Optional[float]:
    """Weighted fusion of visual and description similarity scores.

    If one side is missing (e.g., no description embedding for this event),
@@ -103,7 +102,7 @@ def fuse_scores(
    return VISUAL_WEIGHT * visual_score + DESCRIPTION_WEIGHT * description_score


-def parse_iso_to_timestamp(value: str | None) -> float | None:
+def parse_iso_to_timestamp(value: Optional[str]) -> Optional[float]:
    """Parse an ISO-8601 string as server-local time -> unix timestamp.

    Mirrors the parsing _execute_search_objects uses so both tools accept the
@@ -120,9 +119,9 @@ def parse_iso_to_timestamp(value: str | None) -> float | None:
        return None


-def hydrate_event(event: Event, score: float | None = None) -> dict[str, Any]:
+def hydrate_event(event: Event, score: Optional[float] = None) -> Dict[str, Any]:
    """Convert an Event row into the dict shape returned by find_similar_objects."""
-    data: dict[str, Any] = {
+    data: Dict[str, Any] = {
        "id": event.id,
        "camera": event.camera,
        "label": event.label,
@@ -280,7 +280,7 @@ async def create_face(request: Request, name: str):
    success response with details about the registration, or an error if face recognition
    is not enabled or the image cannot be processed.""",
 )
-def register_face(request: Request, name: str, file: UploadFile):
+async def register_face(request: Request, name: str, file: UploadFile):
    if not request.app.frigate_config.face_recognition.enabled:
        return JSONResponse(
            status_code=400,
@@ -288,7 +288,7 @@ def register_face(request: Request, name: str, file: UploadFile):
        )

    context: EmbeddingsContext = request.app.embeddings
-    result = None if context is None else context.register_face(name, file.file.read())
+    result = None if context is None else context.register_face(name, await file.read())

    if not isinstance(result, dict):
        return JSONResponse(
@@ -313,7 +313,7 @@ def register_face(request: Request, name: str, file: UploadFile):
    registered faces in the system. Returns the recognized face name and confidence score,
    or an error if face recognition is not enabled or the image cannot be processed.""",
 )
-def recognize_face(request: Request, file: UploadFile):
+async def recognize_face(request: Request, file: UploadFile):
    if not request.app.frigate_config.face_recognition.enabled:
        return JSONResponse(
            status_code=400,
@@ -321,7 +321,7 @@ def recognize_face(request: Request, file: UploadFile):
        )

    context: EmbeddingsContext = request.app.embeddings
-    result = context.recognize_face(file.file.read())
+    result = context.recognize_face(await file.read())

    if not isinstance(result, dict):
        return JSONResponse(
@@ -86,15 +86,10 @@ class DebugReplayStopResponse(BaseModel):
 async def start_debug_replay(request: Request, body: DebugReplayStartBody):
    """Start a debug replay session asynchronously."""
    replay_manager = request.app.replay_manager
-    internal_port = request.app.frigate_config.networking.listen.internal
-    if type(internal_port) is str:
-        internal_port = int(internal_port.split(":")[-1])
-
    source = RecordingDebugReplaySource(
        source_camera=body.camera,
        start_ts=body.start_time,
        end_ts=body.end_time,
-        internal_port=internal_port,
    )

    try:
@@ -1,10 +1,12 @@
+from typing import Optional
+
 from pydantic import BaseModel


 class AppTimelineHourlyQueryParameters(BaseModel):
-    cameras: str | None = "all"
-    labels: str | None = "all"
-    after: float | None = None
-    before: float | None = None
-    limit: int | None = 200
-    timezone: str | None = "utc"
+    cameras: Optional[str] = "all"
+    labels: Optional[str] = "all"
+    after: Optional[float] = None
+    before: Optional[float] = None
+    limit: Optional[int] = 200
+    timezone: Optional[str] = "utc"
@@ -1,26 +1,28 @@
+from typing import Optional
+
 from pydantic import BaseModel, Field

 DEFAULT_TIME_RANGE = "00:00,24:00"


 class EventsQueryParams(BaseModel):
-    camera: str | None = "all"
-    cameras: str | None = "all"
-    label: str | None = "all"
-    labels: str | None = "all"
-    sub_label: str | None = "all"
-    sub_labels: str | None = "all"
-    attributes: str | None = "all"
-    zone: str | None = "all"
-    zones: str | None = "all"
-    limit: int | None = 100
-    after: float | None = None
-    before: float | None = None
-    time_range: str | None = DEFAULT_TIME_RANGE
-    has_clip: int | None = None
-    has_snapshot: int | None = None
-    in_progress: int | None = None
-    include_thumbnails: int | None = Field(
+    camera: Optional[str] = "all"
+    cameras: Optional[str] = "all"
+    label: Optional[str] = "all"
+    labels: Optional[str] = "all"
+    sub_label: Optional[str] = "all"
+    sub_labels: Optional[str] = "all"
+    attributes: Optional[str] = "all"
+    zone: Optional[str] = "all"
+    zones: Optional[str] = "all"
+    limit: Optional[int] = 100
+    after: Optional[float] = None
+    before: Optional[float] = None
+    time_range: Optional[str] = DEFAULT_TIME_RANGE
+    has_clip: Optional[int] = None
+    has_snapshot: Optional[int] = None
+    in_progress: Optional[int] = None
+    include_thumbnails: Optional[int] = Field(
        1,
        description=(
            "Deprecated. Thumbnail data is no longer included in the response. "
@@ -28,25 +30,25 @@ class EventsQueryParams(BaseModel):
        ),
        deprecated=True,
    )
-    favorites: int | None = None
-    min_score: float | None = None
-    max_score: float | None = None
-    min_speed: float | None = None
-    max_speed: float | None = None
-    recognized_license_plate: str | None = "all"
-    is_submitted: int | None = None
-    min_length: float | None = None
-    max_length: float | None = None
-    event_id: str | None = None
-    sort: str | None = None
-    timezone: str | None = "utc"
+    favorites: Optional[int] = None
+    min_score: Optional[float] = None
+    max_score: Optional[float] = None
+    min_speed: Optional[float] = None
+    max_speed: Optional[float] = None
+    recognized_license_plate: Optional[str] = "all"
+    is_submitted: Optional[int] = None
+    min_length: Optional[float] = None
+    max_length: Optional[float] = None
+    event_id: Optional[str] = None
+    sort: Optional[str] = None
+    timezone: Optional[str] = "utc"


 class EventsSearchQueryParams(BaseModel):
-    query: str | None = None
-    event_id: str | None = None
-    search_type: str | None = "thumbnail"
-    include_thumbnails: int | None = Field(
+    query: Optional[str] = None
+    event_id: Optional[str] = None
+    search_type: Optional[str] = "thumbnail"
+    include_thumbnails: Optional[int] = Field(
        1,
        description=(
            "Deprecated. Thumbnail data is no longer included in the response. "
@@ -54,28 +56,28 @@ class EventsSearchQueryParams(BaseModel):
        ),
        deprecated=True,
    )
-    limit: int | None = 50
-    cameras: str | None = "all"
-    labels: str | None = "all"
-    sub_labels: str | None = "all"
-    attributes: str | None = "all"
-    zones: str | None = "all"
-    after: float | None = None
-    before: float | None = None
-    time_range: str | None = DEFAULT_TIME_RANGE
-    has_clip: bool | None = None
-    has_snapshot: bool | None = None
-    is_submitted: bool | None = None
-    timezone: str | None = "utc"
-    min_score: float | None = None
-    max_score: float | None = None
-    min_speed: float | None = None
-    max_speed: float | None = None
-    recognized_license_plate: str | None = "all"
-    sort: str | None = None
+    limit: Optional[int] = 50
+    cameras: Optional[str] = "all"
+    labels: Optional[str] = "all"
+    sub_labels: Optional[str] = "all"
+    attributes: Optional[str] = "all"
+    zones: Optional[str] = "all"
+    after: Optional[float] = None
+    before: Optional[float] = None
+    time_range: Optional[str] = DEFAULT_TIME_RANGE
+    has_clip: Optional[bool] = None
+    has_snapshot: Optional[bool] = None
+    is_submitted: Optional[bool] = None
+    timezone: Optional[str] = "utc"
+    min_score: Optional[float] = None
+    max_score: Optional[float] = None
+    min_speed: Optional[float] = None
+    max_speed: Optional[float] = None
+    recognized_license_plate: Optional[str] = "all"
+    sort: Optional[str] = None


 class EventsSummaryQueryParams(BaseModel):
-    timezone: str | None = "utc"
-    has_clip: int | None = None
-    has_snapshot: int | None = None
+    timezone: Optional[str] = "utc"
+    has_clip: Optional[int] = None
+    has_snapshot: Optional[int] = None
@@ -1,4 +1,5 @@
 from enum import Enum
+from typing import Optional

 from pydantic import BaseModel

@@ -16,33 +17,33 @@ class Extension(str, Enum):


 class MediaLatestFrameQueryParams(BaseModel):
-    bbox: int | None = None
-    timestamp: int | None = None
-    zones: int | None = None
-    mask: int | None = None
-    motion: int | None = None
-    paths: int | None = None
-    regions: int | None = None
-    quality: int | None = 70
-    height: int | None = None
-    store: int | None = None
+    bbox: Optional[int] = None
+    timestamp: Optional[int] = None
+    zones: Optional[int] = None
+    mask: Optional[int] = None
+    motion: Optional[int] = None
+    paths: Optional[int] = None
+    regions: Optional[int] = None
+    quality: Optional[int] = 70
+    height: Optional[int] = None
+    store: Optional[int] = None


 class MediaEventsSnapshotQueryParams(BaseModel):
-    download: bool | None = False
-    timestamp: int | None = None
-    bbox: int | None = None
-    crop: int | None = None
-    height: int | None = None
-    quality: int | None = None
+    download: Optional[bool] = False
+    timestamp: Optional[int] = None
+    bbox: Optional[int] = None
+    crop: Optional[int] = None
+    height: Optional[int] = None
+    quality: Optional[int] = None


 class MediaMjpegFeedQueryParams(BaseModel):
    fps: int = 3
    height: int = 360
-    bbox: int | None = None
-    timestamp: int | None = None
-    zones: int | None = None
-    mask: int | None = None
-    motion: int | None = None
-    regions: int | None = None
+    bbox: Optional[int] = None
+    timestamp: Optional[int] = None
+    zones: Optional[int] = None
+    mask: Optional[int] = None
+    motion: Optional[int] = None
+    regions: Optional[int] = None
@@ -1,19 +1,21 @@
+from typing import Optional, Union
+
 from pydantic import BaseModel
 from pydantic.json_schema import SkipJsonSchema


 class MediaRecordingsSummaryQueryParams(BaseModel):
    timezone: str = "utc"
-    cameras: str | None = "all"
+    cameras: Optional[str] = "all"


 class MediaRecordingsAvailabilityQueryParams(BaseModel):
    cameras: str = "all"
-    before: float | SkipJsonSchema[None] = None
-    after: float | SkipJsonSchema[None] = None
+    before: Union[float, SkipJsonSchema[None]] = None
+    after: Union[float, SkipJsonSchema[None]] = None
    scale: int = 30


 class RecordingsDeleteQueryParams(BaseModel):
-    keep: str | None = None
-    cameras: str | None = "all"
+    keep: Optional[str] = None
+    cameras: Optional[str] = "all"
@@ -1,11 +1,13 @@
+from typing import Optional
+
 from pydantic import BaseModel, Field

 from frigate.events.types import RegenerateDescriptionEnum


 class RegenerateQueryParameters(BaseModel):
-    source: RegenerateDescriptionEnum | None = RegenerateDescriptionEnum.thumbnails
-    force: bool | None = Field(
+    source: Optional[RegenerateDescriptionEnum] = RegenerateDescriptionEnum.thumbnails
+    force: Optional[bool] = Field(
        default=False,
        description="Force (re)generating the description even if GenAI is disabled for this camera.",
    )
@@ -1,3 +1,5 @@
+from typing import Union
+
 from pydantic import BaseModel
 from pydantic.json_schema import SkipJsonSchema

@@ -8,11 +10,11 @@ class ReviewQueryParams(BaseModel):
    cameras: str = "all"
    labels: str = "all"
    zones: str = "all"
-    reviewed: int | SkipJsonSchema[None] = None
-    limit: int | SkipJsonSchema[None] = None
-    severity: SeverityEnum | SkipJsonSchema[None] = None
-    before: float | SkipJsonSchema[None] = None
-    after: float | SkipJsonSchema[None] = None
+    reviewed: Union[int, SkipJsonSchema[None]] = None
+    limit: Union[int, SkipJsonSchema[None]] = None
+    severity: Union[SeverityEnum, SkipJsonSchema[None]] = None
+    before: Union[float, SkipJsonSchema[None]] = None
+    after: Union[float, SkipJsonSchema[None]] = None


 class ReviewSummaryQueryParams(BaseModel):
@@ -24,6 +26,6 @@ class ReviewSummaryQueryParams(BaseModel):

 class ReviewActivityMotionQueryParams(BaseModel):
    cameras: str = "all"
-    before: float | SkipJsonSchema[None] = None
-    after: float | SkipJsonSchema[None] = None
+    before: Union[float, SkipJsonSchema[None]] = None
+    after: Union[float, SkipJsonSchema[None]] = None
    scale: int = 30
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Josh Hawkins	07ca84ad0a	add model fetcher endpoint for genai config ui	2026-05-20 08:47:49 -05:00
Josh Hawkins	237eb64011	fix admin response cache leak to non-admin users via nginx proxy_cache	2026-05-20 07:05:48 -05:00
Josh Hawkins	fdc621e3e9	remove camera ui section in settings no point to having it anymore with profiles and camera management settings	2026-05-19 18:46:54 -05:00
Josh Hawkins	4a22448408	tweak collapsible	2026-05-19 18:38:57 -05:00
Josh Hawkins	f0271a802f	fix test	2026-05-19 18:25:20 -05:00
Josh Hawkins	e2128e09ae	change agents title	2026-05-19 18:21:05 -05:00
Josh Hawkins	9e7ad7a9bd	change replay confg dialog to platform aware sheet	2026-05-19 18:15:21 -05:00
Nicolas Mowen	58d66bf17a	Symlink for various AI files	2026-05-19 16:54:34 -06:00
Josh Hawkins	14318357c6	render orphaned filter entries as collapsibles instead of the Key/Value editor	2026-05-19 17:51:28 -05:00