Refactor detector and model management

Refactor Birdseye activity types as composable booleans (#23940 )
* Add combined motion and object Birdseye mode Add a motion_objects mode that keeps Birdseye active when motion is detected or a confirmed tracked object is present, including stationary objects. Wire the mode through configuration, runtime commands, API schemas, documentation, and UI labels. Exclude false-positive trackers and add regression coverage for Birdseye activation and MQTT validation. * Refactor Birdseye activity types as booleans Replace combination-specific Birdseye modes with composable boolean activity types for motion, active objects, stationary objects, and continuous display. Preserve legacy single-mode configuration and MQTT inputs, support canonical comma-separated MQTT combinations, and allow scalar YAML values to be replaced by nested settings through the config API. * Preserve OpenVINO config translations Regenerate the configuration translations with the OpenVINO detector schema available so the unrelated production detector labels remain intact. * Preserve partial Birdseye mode overrides Allow an empty activity selection with a canonical NONE MQTT state so partial camera and profile overrides can disable inherited flags without failing validation. Add regression coverage for camera and profile inheritance, document the NONE contract, and keep the generated schema fixture scoped to Birdseye. * Address Birdseye activity review feedback Move scalar mode compatibility into the 0.18-1 config migration and reject empty activity selections instead of publishing a NONE state. Pass activity signals through a frozen dataclass, preserve existing active-object tracker behavior, and require confirmed stationary objects. Revert the generic YAML mutation and cover migration, inheritance, MQTT, and activation regressions. * Move Birdseye migration to 0.19 Use the 0.19-0 configuration revision for converting scalar Birdseye modes to composable activity flags, and update the migration regression coverage accordingly. * Remove Birdseye migration test Drop the dedicated config migration test as requested during review while retaining the 0.19-0 migration implementation.
2026-10-15 03:02:49 +03:00 · 2026-08-14 14:00:00 -06:00 · 2026-08-14 10:05:08 -05:00 · 2026-08-14 10:05:08 -05:00 · 2026-08-14 10:05:08 -05:00 · 2026-08-14 10:05:08 -05:00
539 changed files with 5882 additions and 34387 deletions
@@ -1,7 +1,7 @@
 default_target: local

 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.18.2
+VERSION = 0.19.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty
@@ -14,10 +14,7 @@ location /auth {
    proxy_pass_request_headers off;
    # Pass info about the request
    proxy_set_header X-Original-Method $request_method;
-    proxy_set_header X-Original-URL $request_uri;
-    # set by the go2rtc stream locations; client headers are not forwarded, so
-    # this cannot be spoofed
-    proxy_set_header X-Go2rtc-Stream-Proxy $go2rtc_stream_proxy;
+    proxy_set_header X-Original-URL $scheme://$http_host$request_uri;
    proxy_set_header X-Server-Port $server_port;
    proxy_set_header Content-Length "";
    # Pass along auth related info
@@ -150,9 +150,7 @@ http {
            include auth_request.conf;
            types {
                video/mp4 mp4;
-                image/jpeg jpg jpeg;
-                image/png png;
-                image/webp webp;
+                image/jpeg jpg;
            }

            expires 7d;
@@ -202,8 +200,6 @@ http {

        # frigate lovelace card uses this path
        location /live/mse/api/ws {
-            # tells /auth to apply the go2rtc stream checks
-            set $go2rtc_stream_proxy 1;
            include auth_request.conf;
            limit_except GET {
                deny  all;
@@ -213,8 +209,6 @@ http {
        }

        location /live/webrtc/api/ws {
-            # tells /auth to apply the go2rtc stream checks
-            set $go2rtc_stream_proxy 1;
            include auth_request.conf;
            limit_except GET {
                deny  all;
@@ -235,8 +229,6 @@ http {

        # integration uses this to add webrtc candidate
        location /api/go2rtc/webrtc {
-            # tells /auth to apply the go2rtc stream checks
-            set $go2rtc_stream_proxy 1;
            include auth_request.conf;
            limit_except POST {
                deny  all;
@@ -2,7 +2,7 @@
 proxy_set_header Host $host;
 proxy_set_header Upgrade $http_upgrade;
 proxy_set_header Connection "Upgrade";
-proxy_set_header X-Original-URL $request_uri;
+proxy_set_header X-Original-URL $scheme://$http_host$request_uri;
 proxy_set_header X-Forwarded-Proto $scheme;
 proxy_set_header X-Forwarded-Host $http_host;
 proxy_set_header X-Forwarded-URI $request_uri;
@@ -56,17 +56,6 @@ mqtt:
  # 2 = exactly once
  qos: 0

-# Optional: Detectors configuration. Defaults to a single CPU detector
-detectors:
-  # Required: name of the detector
-  detector_name:
-    # Required: type of the detector
-    # Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
-    # Additional detector types can also be plugged in.
-    # Detectors may require additional configuration.
-    # Refer to the Detectors configuration page for more information.
-    type: cpu
-
 # Optional: Database configuration
 database:
  # The path to store the SQLite DB (default: shown below)
@@ -157,44 +146,56 @@ auth:
      - front_door
      - back_yard

-# Optional: model modifications
+# Optional: object detection models. Defaults to a single model on a CPU detector.
 # NOTE: The default values are for the EdgeTPU detector.
 # Other detectors will require the model config to be set.
-model:
-  # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
-  path: /edgetpu_model.tflite
-  # Required: path to the labelmap (default: shown below)
-  labelmap_path: /labelmap.txt
-  # Required: Object detection model input width (default: shown below)
-  width: 320
-  # Required: Object detection model input height (default: shown below)
-  height: 320
-  # Required: Object detection model input colorspace
-  # Valid values are rgb, bgr, or yuv. (default: shown below)
-  input_pixel_format: rgb
-  # Required: Object detection model input tensor format
-  # Valid values are nhwc, nchw, hwnc, or hwcn (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 architecture, used by detectors that support more
-  # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
-  # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
-  model_type: ssd
-  # Required: Label name modifications. These are merged into the standard labelmap.
-  labelmap:
-    2: vehicle
-  # Optional: Map of object labels to their attribute labels (default: depends on model)
-  attributes_map:
-    person:
-      - amazon
-      - face
-    car:
-      - amazon
-      - fedex
-      - license_plate
-      - ups
+models:
+    # Optional: the camera environment this model is for (default: shown below)
+    # Cameras select a model by setting detect -> scene to a matching value, and
+    # a model with a scene of all is used by any camera that does not set one.
+    # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+  - scene: all
+    # Required: hardware this model runs on, as <detector> or <detector>:<device>
+    # See https://docs.frigate.video/configuration/object_detectors for the
+    # detectors available and the devices each one accepts. All of a model's
+    # devices must use the same detector. Listing the same device more than once
+    # runs additional inference processes on it.
+    devices:
+      - edgetpu:pci:0
+    # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
+    path: /edgetpu_model.tflite
+    # Required: path to the labelmap (default: shown below)
+    labelmap_path: /labelmap.txt
+    # Required: Object detection model input width (default: shown below)
+    width: 320
+    # Required: Object detection model input height (default: shown below)
+    height: 320
+    # Required: Object detection model input colorspace
+    # Valid values are rgb, bgr, or yuv. (default: shown below)
+    input_pixel_format: rgb
+    # Required: Object detection model input tensor format
+    # Valid values are nhwc, nchw, hwnc, or hwcn (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 architecture, used by detectors that support more
+    # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
+    # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
+    model_type: ssd
+    # Required: Label name modifications. These are merged into the standard labelmap.
+    labelmap:
+      2: vehicle
+    # Optional: Map of object labels to their attribute labels (default: depends on model)
+    attributes_map:
+      person:
+        - amazon
+        - face
+      car:
+        - amazon
+        - fedex
+        - license_plate
+        - ups

 # Optional: Audio Events Configuration
 # NOTE: Can be overridden at the camera level
@@ -251,11 +252,17 @@ birdseye:
  # Optional: Encoding quality of the mpeg1 feed (default: shown below)
  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
  quality: 8
-  # Optional: Mode of the view. Available options are: objects, motion, and continuous
-  #   objects - cameras are included if they have had a tracked object within the last 30 seconds
-  #   motion - cameras are included if motion was detected in the last 30 seconds
-  #   continuous - all cameras are included always
-  mode: objects
+  # Optional: Activity types that include cameras in Birdseye (default: shown below)
+  # Multiple activity types can be enabled at the same time.
+  mode:
+    # Optional: All cameras are included always (default: shown below)
+    continuous: False
+    # Optional: Cameras are included if motion was detected in the last 30 seconds (default: shown below)
+    motion: False
+    # Optional: Cameras are included if they have had an active tracked object within the last 30 seconds (default: shown below)
+    objects: True
+    # Optional: Cameras are included while they have a stationary tracked object (default: shown below)
+    stationary_objects: False
  # Optional: Threshold for camera activity to stop showing camera (default: shown below)
  inactivity_threshold: 30
  # Optional: Configure the birdseye layout
@@ -286,7 +293,7 @@ ffmpeg:
    # Optional: output args for detect streams (default: shown below)
    detect: -threads 2 -f rawvideo -pix_fmt yuv420p
    # Optional: output args for record streams (default: shown below)
-    record: preset-record-generic-audio-aac
+    record: preset-record-generic
  # Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
  # If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
  # If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
@@ -306,6 +313,10 @@ detect:
  width: 1280
  # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
  height: 720
+  # Optional: the environment this camera looks at, which picks the model it runs on
+  # (default: the model with a scene of all)
+  # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
+  scene: outdoor
  # Optional: desired fps for your camera for the input with the detect role (default: shown below)
  # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
  fps: 5
@@ -192,12 +192,14 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and open

 ```yaml
 # Optional: model config
-model:
-  path: /path/to/model
-  width: 320
-  height: 320
-  input_tensor: "nhwc"
-  input_pixel_format: "bgr"
+models:
+  - devices:
+      - openvino:GPU
+    path: /path/to/model
+    width: 320
+    height: 320
+    input_tensor: "nhwc"
+    input_pixel_format: "bgr"
 ```

 </TabItem>
@@ -214,15 +216,15 @@ If the labelmap is customized then the labels used for alerts will need to be ad
 The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.

 ```yaml
-model:
-  labelmap:
-    2: vehicle
-    3: vehicle
-    5: vehicle
-    7: vehicle
-    15: animal
-    16: animal
-    17: animal
+models:
+  - labelmap:
+      2: vehicle
+      3: vehicle
+      5: vehicle
+      7: vehicle
+      15: animal
+      16: animal
+      17: animal
 ```

 Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
@@ -18,13 +18,14 @@ Each camera tile in Birdseye is composed from the frames of the stream assigned

 ## Birdseye Behavior

-### Birdseye Modes
+### Birdseye Activity Types

-Birdseye offers different modes to customize which cameras show under which circumstances.
+Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be enabled together.

- **continuous:** All cameras are always included
- **motion:** Cameras that have detected motion within the last 30 seconds are included
- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
+- **continuous:** The camera is always included
+- **motion:** The camera is included when motion was detected within the last 30 seconds
+- **objects:** The camera is included when an active object was tracked within the last 30 seconds
+- **stationary_objects:** The camera is included while a stationary object is tracked

 ### Custom Birdseye Icon

@@ -39,27 +40,30 @@ To include a camera in Birdseye view only for specific circumstances, or exclude

 **Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.

-**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
+**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types 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               |
+| **Activity types**     | Conditions that determine when to show the camera          |

 </TabItem>
 <TabItem value="yaml">

-```yaml {8-10,12-14}
+```yaml {8-11,13-15}
 # Include all cameras by default in Birdseye view
 birdseye:
  enabled: True
-  mode: continuous
+  mode:
+    continuous: True

 cameras:
  front:
    # Only include the "front" camera in Birdseye view when objects are detected
    birdseye:
-      mode: objects
+      mode:
+        continuous: False
+        objects: True
  back:
    # Exclude the "back" camera from Birdseye view
    birdseye:
@@ -234,7 +234,7 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
 | Amcrest ASH21                |      ✅      |      ❌      | ONVIF service port: 80                                                                                                                                                                                                                                            |
 | Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                                                                                                                                              |
 | Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                                                                                                                                              |
-| Annke CZ504                  |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
+| Annke CZ504                  |      ✅      |      ✅      | Annke support provide specific firmware ([V5.7.1 build 250227](https://github.com/pierrepinon/annke_cz504/raw/refs/heads/main/digicap_V5-7-1_build_250227.dav)) to fix issue with ONVIF "TranslationSpaceFov"                                                     |
 | Axis Q-6155E                 |      ✅      |      ❌      | ONVIF service port: 80; Camera does not support MoveStatus.                                                                                                                                                                                                       |
 | Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Dahua                        |      ✅      |      ✅      | Some low-end Dahuas (lite series, picoo series (commonly), among others) have been reported to not support autotracking. These models usually don't have a four digit model number with chassis prefix and options postfix (e.g. DH-P5AE-PV vs DH-SD49825GB-HNR). |
@@ -172,10 +172,9 @@ mqtt:
 ffmpeg:
  hwaccel_args: preset-rpi-64-h264

-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb

 record:
  enabled: True
@@ -249,10 +248,9 @@ mqtt:
 ffmpeg:
  hwaccel_args: preset-vaapi

-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb

 record:
  enabled: True
@@ -329,15 +327,12 @@ mqtt:
 ffmpeg:
  hwaccel_args: preset-vaapi

-detectors:
-  ov:
-    type: openvino
-    device: AUTO
-
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
+models:
+  - devices:
+      - openvino:AUTO
+    width: 300
+    height: 300
+    input_tensor: nhwc
  input_pixel_format: bgr
  path: /openvino-model/ssdlite_mobilenet_v2.xml
  labelmap_path: /openvino-model/coco_91cl_bkgr.txt
@@ -59,24 +59,19 @@ Running Generative AI models on CPU is not recommended, as high inference times

 ### Recommended Local Models

-#### Vision models
-
 You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:

-| Model               | Notes                                                                                                                                                                |
-| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `qwen3-vl`          | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.6`/`qwen3.8` | 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.                                     |
-
-#### Embedding models
+| Model      | Notes                                                                                                                                                                |
+| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
+| `qwen3.6`  | 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.                                     |

 The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.

-| Model                | Notes                                                                                                                                                                  |
-| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `embeddinggemma-2`   | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Strong semantic search accuracy with efficient inference on a small model. |
-| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Good performance, large model that requires strong hardware for inference. |
+| Model                | Notes                                                                                                                                                               |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |

 :::info

@@ -132,8 +132,7 @@ ffmpeg:

 Frigate reads Intel GPU utilization directly from the kernel's per-client DRM usage counters exposed at `/proc/<pid>/fdinfo/<fd>`. This requires:

- An Intel GPU from Broadwell (5th gen) or newer. Older GPUs do not expose these counters on any kernel.
- Linux kernel **5.19 or newer** for the `i915` driver (**6.5 or newer** on 12th gen and newer), or any release of the `xe` driver.
+- Linux kernel **5.19 or newer** for the `i915` driver, or any release of the `xe` driver.
 - Frigate running with permission to read other processes' fdinfo. Running as root inside the container (the default) satisfies this; non-root setups may need `CAP_SYS_PTRACE`.

 No `intel_gpu_top` binary, `CAP_PERFMON`, privileged mode, or `perf_event_paranoid` tuning is required.
@@ -499,7 +498,7 @@ cameras:

 ## Synaptics

-Hardware accelerated video de-/encoding is supported on Synaptics SL-series SoC.
+Hardware accelerated video de-/encoding is supported on Synpatics SL-series SoC.

 ### Prerequisites

@@ -8,7 +8,7 @@ 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`, `motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#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.

 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.

@@ -24,7 +24,7 @@ When a plate is recognized, the details are:
 - Viewable in the Details pane in Review/History.
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
 - Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.

 ## Model Requirements
@@ -35,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use

 :::note

-In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.

 :::

@@ -86,7 +86,7 @@ cameras:
 </TabItem>
 </ConfigTabs>

-For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.

 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.

@@ -158,7 +158,7 @@ lpr:

 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.

- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
+- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.

 </TabItem>
@@ -316,7 +316,7 @@ lpr:

 :::note

-If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:

 <ConfigTabs>
 <TabItem value="ui">
@@ -378,10 +378,10 @@ Navigate to <NavPath path="Settings > Camera configuration > Object detection" /

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

-| Field                                                     | Description         |
-| --------------------------------------------------------- | ------------------- |
-| **Objects to track**                                      | Add `license_plate` |
-| **Object filters > License Plate > Confidence threshold** | Set to `0.7`        |
+| Field                                          | Description         |
+| ---------------------------------------------- | ------------------- |
+| **Objects to track**                           | Add `license_plate` |
+| **Object filters > License Plate > Threshold** | Set to `0.7`        |

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

@@ -456,7 +456,7 @@ With this setup:
 - Snapshots will have license plate bounding boxes on them.
 - The `frigate/events` MQTT topic will publish tracked object updates.
 - Debug view will display `license_plate` bounding boxes.
- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.

 ### Using the Secondary LPR Pipeline (Without Frigate+)

@@ -611,9 +611,9 @@ If you are still having issues detecting plates, start with a basic configuratio

 </FaqItem>

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

-In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.

 </FaqItem>

@@ -699,7 +699,7 @@ lpr:
 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 vehicle's label will change to the recognized plate when LPR is enabled and working.
+   - 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>
@@ -714,13 +714,13 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB

 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.

-If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.

 </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?">

-This could happen if vehicles 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.
+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.

 If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.

@@ -29,7 +29,6 @@ Frigate supports multiple different detectors that work on different types of ha

 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
- <CommunityBadge /> [XDNA2](#amd-xdna2): AMD Ryzen AI / XDNA2 NPUs can run object detection through the community-maintained `frigate-xdna` ZMQ sidecar.

 **Apple Silicon**

@@ -69,12 +68,66 @@ Frigate supports multiple different detectors that work on different types of ha

 :::note

-Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
+A single model can not be spread across different detector types (ex: OpenVINO and Coral EdgeTPU can not run the same model at the same time). Configuring more than one model, each on its own detector type, is supported.

 This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)

 :::

+### Configuring models and hardware
+
+Object detection is configured with a `models` list. Each entry describes one model and the hardware it runs on:
+
+```yaml
+models:
+  - devices:
+      - openvino:GPU
+    path: /config/model_cache/yolov9-s.onnx
+    model_type: yolo-generic
+    width: 320
+    height: 320
+```
+
+Each entry in `devices` is a detector type, optionally followed by a colon and a device for that detector, such as `edgetpu:pci:0`, `openvino:NPU`, or `tensorrt:0`. The per-detector sections below document the device values each one accepts. Listing several devices runs the model on all of them, and listing the **same** device more than once runs additional inference processes against it, which can improve throughput on hardware that keeps up with more than one stream:
+
+```yaml
+models:
+  - devices:
+      - openvino:GPU
+      - openvino:GPU
+```
+
+Coral EdgeTPU and MemryX accelerators can only be opened by one process, so those devices can not be repeated.
+
+### Running more than one model
+
+Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
+
+```yaml
+models:
+  - scene: outdoor
+    path: plus://your-outdoor-model
+    devices:
+      - edgetpu:pci:0
+  - scene: indoor
+    path: /config/model_cache/indoor.onnx
+    model_type: yolo-generic
+    devices:
+      - openvino:GPU
+
+cameras:
+  driveway:
+    detect:
+      scene: outdoor
+    ...
+  hallway:
+    detect:
+      scene: indoor
+    ...
+```
+
+Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
+
 ### Choosing a model size

 Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
@@ -93,11 +146,11 @@ The best detection accuracy comes from a model trained on images that look like

 # Officially Supported Detectors

-Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. Each of a model's devices runs in a dedicated process, and they pull from a common queue of detection requests from the cameras assigned to that model.

 ## Edge TPU Detector

-The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
+The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To use it, prefix a model's device with `edgetpu`.

 The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.

@@ -118,10 +171,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">

 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb
 ```

 </TabItem>
@@ -138,13 +190,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">

 ```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: usb:0
-  coral2:
-    type: edgetpu
-    device: usb:1
+models:
+  - devices:
+      - edgetpu:usb:0
+      - edgetpu:usb:1
 ```

 </TabItem>
@@ -163,10 +212,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">

 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: ""
+models:
+  - devices:
+      - 'edgetpu:'
 ```

 </TabItem>
@@ -183,10 +231,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">

 ```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: pci
+models:
+  - devices:
+      - edgetpu:pci
 ```

 </TabItem>
@@ -203,13 +250,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">

 ```yaml
-detectors:
-  coral1:
-    type: edgetpu
-    device: pci:0
-  coral2:
-    type: edgetpu
-    device: pci:1
+models:
+  - devices:
+      - edgetpu:pci:0
+      - edgetpu:pci:1
 ```

 </TabItem>
@@ -226,13 +270,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
 <TabItem value="yaml">

 ```yaml
-detectors:
-  coral_usb:
-    type: edgetpu
-    device: usb
-  coral_pci:
-    type: edgetpu
-    device: pci
+models:
+  - devices:
+      - edgetpu:usb
+      - edgetpu:pci
 ```

 </TabItem>
@@ -274,7 +315,7 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc

 ## OpenVINO Detector

-The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
+The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To use it, prefix a model's device with `openvino`.

 The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.

@@ -287,13 +328,10 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:

 ```yaml
-detectors:
-  ov_0:
-    type: openvino
-    device: GPU # or NPU
-  ov_1:
-    type: openvino
-    device: GPU # or NPU
+models:
+  - devices:
+      - openvino:GPU # or NPU
+      - openvino:GPU # or NPU
 ```

 :::
@@ -314,6 +352,12 @@ Intel NPUs cannot be used under Home Assistant OS, which does not include the NP

 ## Apple Silicon detector

+:::warning
+
+The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
 The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.

 ### Setup {#setup-apple-silicon}
@@ -454,11 +498,10 @@ If the correct build is used for your GPU then the GPU will be detected and used
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:

 ```yaml
-detectors:
-  onnx_0:
-    type: onnx
-  onnx_1:
-    type: onnx
+models:
+  - devices:
+      - onnx
+      - onnx
 ```

 :::
@@ -471,7 +514,7 @@ detectors:

 ## CPU Detector (not recommended)

-The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
+The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To use it, set a model's device to `cpu`.

 :::danger

@@ -481,7 +524,7 @@ The CPU detector is not recommended for general use. If you do not have GPU or E

 The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`

-A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with the model's `path`.

 ### Configuration {#configuration-cpu}

@@ -491,6 +534,12 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d

 ## Deepstack / CodeProject.AI Server Detector

+:::warning
+
+The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
 The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.

 ### Setup {#setup-deepstack}
@@ -509,28 +558,6 @@ To verify that the integration is working correctly, start Frigate and observe t

 # Community Supported Detectors

-## AMD XDNA2
-
-AMD Ryzen AI / XDNA2 NPUs can be used through the community-maintained
-[frigate-xdna](https://github.com/mitchins/frigate-xdna) detector sidecar.
-The sidecar runs separately from Frigate and connects using Frigate's ZMQ
-detector interface.
-
-Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2 devices
-are not yet qualified; XDNA1 is unsupported.
-
-Follow the frigate-xdna setup instructions to prepare and start the sidecar
-before starting Frigate.
-
-### Configuration {#configuration-xdna2}
-
-Using the detector config below will connect Frigate to the sidecar:
-
-<ModelConfigDropdown detectorTitle="AMD XDNA2" models={objectDetectorsModels.xdna2.models} />
-
-The example assumes Frigate and the sidecar share a Docker network where the
-sidecar is named `xdna`.
-
 ## MemryX MX3

 This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.
@@ -575,7 +602,7 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht

 3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.

-4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
+4. Bind-mount the `.zip` file into the container and specify its path using the model's `path` in your config.

 5. Update `labelmap_path` to match your custom model's labels.

@@ -705,13 +732,10 @@ If no custom model is provided, the RKNN detector downloads a default model from
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:

 ```yaml
-detectors:
-  rknn_0:
-    type: rknn
-    num_cores: 0
-  rknn_1:
-    type: rknn
-    num_cores: 0
+models:
+  - devices:
+      - rknn:0
+      - rknn:0
 ```

 :::
@@ -785,6 +809,101 @@ Explanation of the parameters:
  - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
 - `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).

+<<<<<<< HEAD
+=======
+## DeGirum
+
+:::warning
+
+The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
+
+:::
+
+DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
+
+### Configuration {#configuration-degirum}
+
+#### AI Server Inference
+
+Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
+
+```yaml
+degirum_detector:
+  container_name: degirum
+  image: degirum/aiserver:latest
+  privileged: true
+  ports:
+    - "8778:8778"
+```
+
+All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
+
+Once completed, configure the detector as follows:
+
+<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
+
+The model is set on the same `models` entry as the DeGirum device. You can set it to:
+
+- A model listed on the [AI Hub](https://hub.degirum.com)
+  - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
+- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
+- A path to some model.json.
+
+```yaml
+models:
+  - devices:
+      - degirum:<location>
+    path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
+    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+```
+
+#### Local Inference
+
+It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
+
+1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
+2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
+3. Create a DeGirum detector in your configuration.
+
+<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
+
+Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
+
+```yaml
+models:
+  - devices:
+      - degirum:<location>
+    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
+    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+```
+
+#### AI Hub Cloud Inference
+
+If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
+
+1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
+2. Get an access token.
+3. Create a DeGirum detector in your configuration.
+
+<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
+
+Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
+
+```yaml
+models:
+  - devices:
+      - degirum:<location>
+    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
+    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+```
+
+>>>>>>> 34363affa (Refactor detector and model management)
 ## AXERA

 Hardware accelerated object detection is supported on the following SoCs:
@@ -45,10 +45,10 @@ Any detection below `min_score` will be immediately thrown out and never tracked

 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set score filters globally.

-| Field                                              | Description                                                      |
-| -------------------------------------------------- | ---------------------------------------------------------------- |
-| **Object filters > Person > Minimum confidence**   | Minimum score for a single detection to initiate tracking        |
-| **Object filters > Person > Confidence threshold** | Minimum computed (median) score to be considered a true positive |
+| Field                                   | Description                                                      |
+| --------------------------------------- | ---------------------------------------------------------------- |
+| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking        |
+| **Object filters > Person > Threshold** | Minimum computed (median) score to be considered a true positive |

 To override score filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.

@@ -103,12 +103,12 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "

 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set shape filters globally.

-| Field                                              | Description                                                              |
-| -------------------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
+| Field                                   | Description                                                              |
+| --------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |

 To override shape filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.

@@ -70,14 +70,14 @@ Object filters help reduce false positives by constraining the size, shape, and

 Navigate to <NavPath path="Settings > Global configuration > Objects" />.

-| Field                                              | Description                                                              |
-| -------------------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
-| **Object filters > Person > Minimum confidence**   | Minimum score for the object to initiate tracking                        |
-| **Object filters > Person > Confidence threshold** | Minimum computed score to be considered a true positive                  |
+| Field                                   | Description                                                              |
+| --------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
+| **Object filters > Person > Min Score** | Minimum score for the object to initiate tracking                        |
+| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive                  |

 To override filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" />.

@@ -191,12 +191,14 @@ cameras:
    detect:
      enabled: false
    record:
-      enabled: true
+      enabled: false
    profiles:
      away:
        enabled: true
        detect:
          enabled: true
+        record:
+          enabled: true
      home:
        enabled: false
 ```
@@ -249,12 +251,6 @@ Leaving the `objects` section empty (or omitting `track`) does not clear the lis

 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.

-### Why can't a profile enable recording when it's disabled in the base config?
-
-Frigate only sets up a camera's recording stream at startup when recording is enabled in the base config, so enabling it later from a profile has no effect. The same applies to turning recording on from the UI or MQTT.
-
-To keep recording off by default, leave `record.enabled: true` in the base config and create a profile that sets `record.enabled: false`. Activate that profile and it will be restored automatically when Frigate starts.
-
 ### 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.
@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";

 Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.

-New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
+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

@@ -291,7 +291,7 @@ For advanced use cases, the [custom export HTTP API](../integrations/api/export-
 POST /export/custom/{camera_name}/start/{start_time}/end/{end_time}
 ```

-The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS) with audio removed (`-an`). When providing your own `ffmpeg_input_args`, include `-an` if you want audio stripped from the export.
+The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS).

 The following example exports a time-lapse at 60x speed with 25 FPS:

@@ -197,7 +197,7 @@ For cameras that support two-way talk, go2rtc will automatically establish an au
 To prevent this, you must configure two separate stream instances:

 1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
-2. A second stream instance with no `#` parameters at all for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
+2. A second stream instance without `#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)

 Configuration example:

@@ -215,8 +215,6 @@ In this configuration:
 - `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
 - `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.

-Any `#` parameter on a bare `rtsp://` source disables the backchannel unless the URL explicitly contains `#backchannel=1`. A two-way talk stream with something like `#video=h264` on it silently loses two-way audio, and Frigate will report that two-way talk is unavailable for that stream.
-
 ## Security: Restricted Stream Sources

 For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.
@@ -245,8 +245,8 @@ Triggers are best configured through the Frigate UI.
 1. Navigate to <NavPath path="Settings > Enrichments > Triggers" /> and select a camera from the dropdown menu.
 2. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
 3. In the **Create Trigger** wizard:
-   - Enter a **Name** for the trigger (e.g., "Red Car Alert"). Frigate derives the trigger's
-     internal **ID** from this name, which can be revealed and edited with the show/hide toggle.
+   - Enter a **Name** for the trigger (e.g., "Red Car Alert").
+   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
   - Select the **Type** (`Thumbnail` or `Description`).
   - For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
   - For `Description`, enter text to trigger this action when a similar tracked object description is detected.
@@ -28,7 +28,7 @@ During testing, enable the Zones option for the [Debug view](/usage/live#the-sin
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Under the **Zones** section, click the plus icon to add a new zone.
 3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
-4. Configure zone options such as **Name**, **Objects**, **Loitering Time**, and **Inertia** in the zone editor.
+4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
 5. Press **Save** when finished.

 </TabItem>
@@ -200,7 +200,7 @@ When using loitering zones, a review item will behave in the following way:

 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone (e.g., `sidewalk`).
-   - Set **Loitering Time** to the desired number of seconds (e.g., `4`)
+   - Set **Loitering time** to the desired number of seconds (e.g., `4`)
   - Under **Objects**, add the relevant object types (e.g., `person`)

 </TabItem>
@@ -291,7 +291,7 @@ Accurate real-world distance measurements are required to estimate speeds. These

 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Create or edit a zone with exactly 4 points aligned to the ground plane.
-3. In the zone editor, enable **Speed Estimation** and enter the real-world **Line A distance**, **Line B distance**, **Line C distance**, and **Line D distance** between each pair of consecutive points.
+3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
   - For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
 4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.

@@ -358,7 +358,7 @@ Zones can be configured with a minimum speed requirement, meaning an object must

 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone with distances configured.
-   - Set **Speed Threshold** to the desired minimum speed (e.g., `20`)
+   - Set **Speed threshold** to the desired minimum speed (e.g., `20`)
   - The unit is kph or mph, depending on the **Unit system** setting

 </TabItem>
@@ -54,7 +54,7 @@ An object filter mask drops any [bounding box](#bounding-box) whose bottom cente

 ## 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. Set with `min_score` in the config, shown as **Minimum confidence** in the settings UI.
+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

@@ -86,7 +86,7 @@ A more specific identity assigned to a [tracked object](#tracked-object-event-in

 ## Threshold

-The median score an object must reach to be considered a true positive. Set with `threshold` in the config, shown as **Confidence threshold** in the settings UI.
+The median score an object must reach to be considered a true positive.

 ## Top Score

@@ -70,9 +70,6 @@ Frigate supports multiple different detectors that work on different types of ha
 - [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)
  - Runs best on discrete AMD GPUs
- <CommunityBadge /> [XDNA2 (Ryzen AI)](#amd-xdna2): AMD XDNA2 NPU (sub-watt power AI/ML processor separate to the GPU) inside Strix and other "AI" branded AMD platforms
-  - Has only been tested with YOLOv9, in theory other graphs may be compiled too.
-  - Runs via ZMQ proxy which adds some latency, only recommended for local connection

 **Apple Silicon**

@@ -299,32 +296,6 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ---------------- | ----------------------------------- |
 | yolov9-tiny      | ~ 4 ms                              |

-### AMD Ryzen AI / XDNA2
-
-Frigate supports AMD XDNA2 NPUs through the community-maintained
-frigate-xdna ZMQ sidecar. It works with stock Frigate and supports
-Frigate+ models or compatible local YOLO ONNX models. Models are compiled
-once on the target system and cached for subsequent use.
-
-Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2
-devices are not yet qualified; XDNA1 is unsupported.
-
-Measured YOLOv9 detector latency on Strix Halo:
-
-| Model | 320 | 640 |
-| ----- | ---: | ---: |
-| YOLOv9-T | ~7.4 ms | unsupported |
-| YOLOv9-S | ~9.0 ms | ~20.0 ms |
-| YOLOv9-M | ~13.1 ms | ~34.4 ms |
-| YOLOv9-C | ~14.1 ms | ~35.2 ms |
-| YOLOv9-E | ~69.4 ms | ~224.8 ms |
-
-**YOLOv9-C at 320 is the recommended quality/performance balance.**
-C at 640 is also usable where the lower throughput is acceptable.
-
-Setup, model preparation, and compatibility details are available
-[in the frigate-xdna documentation](https://github.com/mitchins/frigate-xdna).
-
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)

 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
@@ -78,9 +78,9 @@ Users of the Snapcraft build of Docker cannot use storage locations outside your

 Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is **64MB**.

-Frigate needs about **210MB** for **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in Docker Compose). If raising the shm size does not help, check your [process and file limits](#process-and-file-limits) as well.
+The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in Docker Compose). If raising the shm size does not help, check your [process and file limits](#process-and-file-limits) as well.

-The Frigate container also stores logs and an nginx cache in shm, which can take up to **50MB**. The calculator below includes this, along with room for 2 cameras added while Frigate is running. Its result holds about 25 frames per camera, a little above the 20 frame minimum Frigate warns below. Frigate keeps up to 50 when there is room, which gives slow processes more time to read each frame.
+The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.

 <ShmCalculator/>

@@ -5,7 +5,7 @@ title: Updating

 # Updating Frigate

-The current stable version of Frigate is **0.18.1**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.18.1).
+The current stable version of Frigate is **0.18.0**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.18.0).

 Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant App, etc.). Below are instructions for the most common setups.

@@ -31,21 +31,21 @@ If you’re running Frigate via Docker (recommended method), follow these steps:

 2. **Update and Pull the Latest Image**:
   - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.18.1` instead of `0.17.1`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.18.0` instead of `0.17.1`). For example:
       ```yaml
       services:
         frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.18.1
+           image: ghcr.io/blakeblackshear/frigate:0.18.0
       ```
     - Then pull the image:
       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.18.1
+       docker pull ghcr.io/blakeblackshear/frigate:0.18.0
       ```
     - **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
   - If using `docker run`:
-     - Pull the image with the appropriate tag (e.g., `0.18.1`, `0.18.1-tensorrt`, or `stable`):
+     - Pull the image with the appropriate tag (e.g., `0.18.0`, `0.18.0-tensorrt`, or `stable`):
       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.18.1
+       docker pull ghcr.io/blakeblackshear/frigate:0.18.0
       ```

 3. **Start the Container**:
@@ -100,7 +100,7 @@ If an update causes issues:
 1. Stop Frigate.
 2. Restore your backed-up config file and database.
 3. Revert to the previous image version:
-   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.18.0`) in your `docker run` command.
+   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.17.1`) in your `docker run` command.
   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.4`), and re-run `docker compose up -d`.
   - For Home Assistant: Restore from the app/addon backup you took before you updated.
 4. Verify the old version is running again.
@@ -222,15 +222,12 @@ You need to refer to **Configure hardware acceleration** above to enable the con
 ```yaml {3-6,9-15,20-21}
 mqtt: ...

-detectors: # <---- add detectors
-  ov:
-    type: openvino  # <---- use openvino detector
-    device: GPU
-
-# We will use the default MobileNet_v2 model from OpenVINO.
-model:
-  width: 300
-  height: 300
+models: # <---- add models
+  - devices:
+      - openvino:GPU # <---- use the openvino detector on the GPU
+    # We will use the default MobileNet_v2 model from OpenVINO.
+    width: 300
+    height: 300
  input_tensor: nhwc
  input_pixel_format: bgr
  path: /openvino-model/ssdlite_mobilenet_v2.xml
@@ -281,10 +278,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a
 ```yaml {3-6,11-12}
 mqtt: ...

-detectors: # <---- add detectors
-  coral:
-    type: edgetpu
-    device: usb
+models: # <---- add models
+  - devices:
+      - edgetpu:usb

 cameras:
  name_of_your_camera:
@@ -321,10 +317,9 @@ If you are using YAML to configure Frigate instead of the UI, your configuration
 mqtt:
  enabled: False

-detectors:
-  coral:
-    type: edgetpu
-    device: usb
+models:
+  - devices:
+      - edgetpu:usb

 cameras:
  name_of_your_camera:
@@ -357,7 +352,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
 ```yaml {16-17}
 mqtt: ...

-detectors: ...
+models: ...

 cameras:
  name_of_your_camera:
@@ -11,12 +11,6 @@ MQTT requires a network connection to your broker. This is typically local, but

 :::

-:::note
-
-Wherever a topic below includes a camera, mask, or zone name, use its `ID` from the config, not its `friendly_name`. For example, a camera with `friendly_name: "Back Yard"` and ID `back_yard` publishes to `frigate/back_yard/...`, not `frigate/Back Yard/...`.
-
-:::
-
 ## General Frigate Topics

 ### `frigate/available`
@@ -561,20 +555,21 @@ Topic with current state of Birdseye for a camera. Published values are `ON` and

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

-Topic to set Birdseye mode for a camera. Birdseye offers different modes to customize under which circumstances the camera is shown.
+Topic to set the Birdseye activity types for a camera. Send one uppercase activity type or combine multiple types with commas, for example `MOTION,OBJECTS,STATIONARY_OBJECTS`.

-_Note: Changing the value from `CONTINUOUS` -> `MOTION | OBJECTS` will take up to 30 seconds for
+_Note: Changing the value from `CONTINUOUS` to non-continuous activity types will take up to 30 seconds for
 the camera to be removed from the view._

-| Command      | Description                                                       |
-| ------------ | ----------------------------------------------------------------- |
-| `CONTINUOUS` | Always included                                                   |
-| `MOTION`     | Show when detected motion within the last 30 seconds are included |
-| `OBJECTS`    | Shown if an active object tracked within the last 30 seconds      |
+| Command              | Description                                                      |
+| -------------------- | ---------------------------------------------------------------- |
+| `CONTINUOUS`         | Always included                                                  |
+| `MOTION`             | Shown if motion was detected within the last 30 seconds          |
+| `OBJECTS`            | Shown if an active object was tracked within the last 30 seconds |
+| `STATIONARY_OBJECTS` | Shown while a stationary object is tracked                       |

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

-Topic with current state of the Birdseye mode for a camera. Published values are `CONTINUOUS`, `MOTION`, `OBJECTS`.
+Topic with the current Birdseye activity types for a camera. Multiple enabled types are published as a comma-separated value in the order `OBJECTS`, `MOTION`, `STATIONARY_OBJECTS`, `CONTINUOUS`.

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

@@ -62,10 +62,9 @@ Once you have [requested your first model](../plus/first_model.md) and gotten yo
 You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:

 ```yaml
-detectors: ...
-
-model:
-  path: plus://<your_model_id>
+models:
+  - devices: ...
+    path: plus://<your_model_id>
 ```

 :::note
@@ -79,10 +78,11 @@ Models are downloaded into the `/config/model_cache` folder and only downloaded
 If needed, you can override the labelmap for Frigate+ models. This is not recommended as renaming labels will break the Submit to Frigate+ feature if the labels are not available in Frigate+.

 ```yaml
-model:
-  path: plus://<your_model_id>
-  labelmap:
-    3: animal
-    4: animal
-    5: animal
+models:
+  - devices: ...
+    path: plus://<your_model_id>
+    labelmap:
+      3: animal
+      4: animal
+      5: animal
 ```
@@ -27,10 +27,6 @@ The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant das
 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.

-## [frigate-abr](https://github.com/007hacky007/frigate-abr)
-
-[frigate-abr](https://github.com/007hacky007/frigate-abr) is a drop-in Docker image of Frigate that adds adaptive bitrate (ABR) playback for recordings: a sidecar transcodes footage to lower quality tiers on demand, for reviewing over slow remote connections. Segments are transcoded when played and cached, so no additional stream is recorded. Frigate itself is not modified.
-
 ## [Frigate Notify](https://github.com/0x2142/frigate-notify)

 [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.
@@ -21,13 +21,7 @@ Yes. Models and metadata are stored in the `model_cache` directory within the co

 ### Can I keep using my Frigate+ models even if I do not renew my subscription?

-Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models you train during an active subscription remain licensed for your continued use even after your subscription ends — models already in your model cache will keep working indefinitely. An active subscription is required to train new models and download new versions.
-
-### Can I use Frigate+ models commercially?
-
-A standard subscription covers use on camera systems you own or operate, including for your business. A shop, restaurant, warehouse, or office running Frigate+ at its own locations (including multiple locations) is exactly the kind of use the subscription is for.
-What the standard subscription does not cover is using Frigate+ models to provide a product or service to others. If you're deploying models at your customers' sites, bundling them with hardware you sell, or running them as part of a hosted or managed service, even if your customers never receive the model files themselves, you'll need a commercial license.
-Note that professional installers are fine under standard subscriptions when each customer holds their own Frigate+ subscription. The commercial license is for cases where your license powers your customers' sites.
+Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models trained with your subscription are yours to keep and use forever. However, do note that the terms and conditions prohibit you from sharing, reselling, or creating derivative products from the models.

 ### Why can't I submit images to Frigate+?

@@ -36,10 +36,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" />. In the *
 <TabItem value="yaml">

 ```yaml
-detectors: ...
-
-model:
-  path: plus://<your_model_id>
+models:
+  - devices: ...
+    path: plus://<your_model_id>
 ```

 :::tip
@@ -64,20 +63,20 @@ Frigate+ models generally have much higher scores than the default model provide
 <ConfigTabs>
 <TabItem value="ui">

-Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Minimum confidence** and **Confidence threshold** for each object type, then click **Save**.
+Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.

-| Object            | Minimum confidence | Confidence 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                  |
+| 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">
@@ -65,11 +65,11 @@ Some users may find that Frigate+ models result in more false positives initiall

 Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:

- **People**: `person`, `face`, `baby`
- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
+- **People**: `person`, `face`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
 - **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`, `possum`, `rodent`
- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`

 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.

@@ -77,12 +77,9 @@ Other object types available in the default Frigate model are not available. Add

 Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.

-Where possible, these labels are mapped to existing labels during training. For example, any `duck` labels are mapped to `bird` until support for new labels is added.
+Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.

- **Vehicles**: `tractor`, `golf_cart`, `bus`, `airplane`, `helicopter`, `rickshaw`, `scooter`
- **Delivery Logos**: `bpost`, `auspost`, `aramex`, `transoflex`, `parcelforce`, `hermes`, `cargus`, `fan_courier`, `sameday`, `la_poste`
- **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`
- **Other**: `sports_ball`, `drone`, `lawnmower`
+The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`, `la_poste`, `lawnmower`, `heron`, `rickshaw`, `wombat`, `auspost`, `aramex`, `bobcat`, `mustelid`, `transoflex`, `airplane`, `drone`, `mountain_lion`, `crocodile`, `turkey`, `baby_stroller`, `monkey`, `coyote`, `porcupine`, `parcelforce`, `sheep`, `snake`, `helicopter`, `lizard`, `duck`, `hermes`, `cargus`, `fan_courier`, `sameday`

 Candidate labels are not available for automatic suggestions.

@@ -66,19 +66,17 @@ An FFmpeg message meaning it probed the stream but never saw enough decodable vi

 ## Recording

-<FaqItem id="no-new-recording-segments" question="No new recording segments were created (or: No new valid recording segments were created / No valid segments created since last invalid segment) for <camera> in the last 120s">
+<FaqItem id="no-new-recording-segments" question="No new recording segments were created for <camera> in the last 120s">

-Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
+Frigate's record watchdog is restarting the record FFmpeg process because no valid segment has reached the cache. This means the record stream is not connecting or the segments are being rejected (see the audio-codec entry below).

-See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
+See [Recordings: the record stream isn't connecting](/troubleshooting/recordings#the-record-stream-isnt-connecting).

 </FaqItem>

-<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding. / Discarding a corrupt recording segment / Failed to probe corrupt segment / Invalid recording segment detected">
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding.">

-A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
-
-See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
+A cached recording segment failed validation (no readable video stream) and was deleted. The most common cause is a segment that was truncated because the record FFmpeg process was killed mid-write, so this often appears alongside, and as a consequence of, the record-stream restarts above. A segment containing only audio triggers it too.

 </FaqItem>

@@ -3,31 +3,7 @@ id: cpu
 title: High CPU Usage
 ---

-High CPU usage can impact Frigate's performance and responsiveness. This guide explains how to interpret the CPU values Frigate reports and outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
-
-## Understanding Frigate's Reported CPU Usage
-
-Frigate's CPU percentages often look much higher than what the host reports. Usually both numbers are correct and are simply measured against different denominators, so confirm you actually have a problem before tuning anything.
-
-### Per-process values are relative to a single core
-
-The values Frigate reports for FFmpeg, capture, detect, detector, and other processes follow the same convention as `top`: 100% means one CPU core is fully saturated, not that the whole system is saturated. A multithreaded process such as FFmpeg can legitimately report well over 100%.
-
-Host and hypervisor tools instead report a percentage of the machine's total capacity across all cores. This includes `docker stats`, the `htop` summary, the Proxmox summary graph, the Unraid dashboard, Synology Resource Monitor, and Home Assistant's system monitor sensors. To reconcile the two:
-
-```
-host percentage ≈ (sum of Frigate's process percentages) / (number of cores)
-```
-
-On a 4 core system, an FFmpeg process reporting 100% is consuming one quarter of the machine, so the host will show roughly 25 to 30% once the remaining Frigate processes are included. That same 100% on a 16 core system is about 6%. Frigate's own warning thresholds use the per-core convention as well, so an FFmpeg process is flagged at 20% of a single core, not 20% of the system.
-
-### Instantaneous samples and averages measure different things
-
-Frigate collects stats every 15 seconds, and the `cpu` value covers only the interval since the previous collection. The `cpu_average` value in the stats API and MQTT payload is the average across the entire life of the process, and it is what the high CPU usage warnings are based on. Host dashboards generally plot data averaged over a longer window, so a single Frigate sample can show a peak that a host graph never displays. A process that has just started, such as FFmpeg after a camera reconnect, reports 0 until it has been sampled twice.
-
-### The system-wide value depends on what the container can see
-
-The system CPU value is read from `/proc/stat`. Under Docker that file belongs to the host, so the value covers the entire machine including workloads unrelated to Frigate, and it will not match `docker stats` for the Frigate container. Under an LXC container, lxcfs virtualizes `/proc/stat` and the value reflects only the cores assigned to the container. In a virtual machine, the guest sees only its assigned vCPUs while the hypervisor divides by every physical thread on the node, so guest and host percentages will not agree even when both are accurate.
+High CPU usage can impact Frigate's performance and responsiveness. This guide outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.

 ## 1. Hardware Acceleration for Video Decoding

@@ -96,19 +72,3 @@ The model you use significantly impacts detector performance. Frigate provides d
 - 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).
-
-## 3. Reducing Detector CPU Usage
-
-**Priority: High**
-
-The **Detector CPU Usage** metric measures the CPU spent converting frames into the tensor format the model expects and post-processing the model's output. It does not include inference, so this value can be high even when you've configured a GPU, NPU, or Coral for object detection.
-
-This metric scales with how many detections per second Frigate runs and how expensive each one is to prepare. Tuning [motion detection](../configuration/motion_detection) is usually the first recommendation to reduce the number of detections. Additionally, you can:
-
- **Lower `detect -> fps`.** 5 is the recommended value for nearly all cameras. Running at 10 doubles the frames eligible for detection and is one of the largest contributors to this metric.
- **Use a 320x320 model.** A 640x640 model has 4 times as many pixels to transpose, convert, and copy on every inference.
- **Prefer a model that takes integer input.** Models configured with `input_dtype: float` require each frame to be converted to float32 and normalized on the CPU first. Models taking `int` input, such as the tflite models used by the Edge TPU, skip that step.
- **Do not match the detect resolution to the model resolution.** The detect stream should match your camera's aspect ratio, for example `1280x720`, not the model's input size. Frigate crops and scales regions of motion itself, so an oversized detect stream only adds work.
- **Tune stationary object behavior.** Objects that never settle into a stationary state are re-detected continuously. Raising `detect -> stationary -> interval` reduces how often detection runs on objects that are already parked. See [stationary objects](../configuration/stationary_objects).
-
-Adding [more detector instances](#multiple-detector-instances) spreads this work across more CPU cores, but does not reduce the total CPU used.
@@ -39,20 +39,6 @@ To do this efficiently the following setup is required:

 When this is done correctly, the GPU will do the decoding and scaling which will result in a small increase in CPU usage but with better results.

-### How can I rotate my camera's video feed?
-
-Rotation is best done in the camera's firmware settings (usually called rotate, flip, or corridor mode) so the video arrives already rotated and no extra processing is needed. Check there first.
-
-If your camera does not support rotation, go2rtc's ffmpeg module can rotate the stream with the `#rotate` parameter (`90`, `180`, `270`, or `-90`), but this is not recommended: rotation requires transcoding (re-encoding) the video, which significantly increases CPU usage, especially for high resolution streams.
-
-```yaml
-go2rtc:
-  streams:
-    my_camera: "ffmpeg:rtsp://user:password@192.168.1.10:554/stream#video=h264#hardware#rotate=90"
-```
-
-Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
-
 ### My mjpeg stream or snapshots look green and crazy

 This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.
@@ -147,12 +133,6 @@ cameras:
      height: 720
 ```

-### What is the `version` key in my config file?
-
-`version` records the config format that your config was last migrated to. On startup Frigate compares it against the format the running version expects, and if it is older it copies your config to `/config/backup_config.yaml`, rewrites it to the new format, and updates `version` as the final step. A config with no `version` key is assumed to predate 0.14 and is migrated from there.
-
-Frigate manages this key for you, so do not set or edit it. Raising it makes Frigate skip migrations your config still needs, and lowering it re-runs migrations against config that has already been converted. Either can leave you with a config that no longer validates.
-
 ### 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.
@@ -78,9 +78,7 @@ go2rtc:

 :::warning

-The transcoding modifiers (`#video=`, `#audio=`, `#hardware`, …) **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.
-
-A bare `rtsp://` source reads a different set of modifiers: `#backchannel=`, `#media=`, `#timeout=`, and `#transport=`. These do nothing on an `ffmpeg:` source. Adding **any** modifier to a bare `rtsp://` source also disables the camera's backchannel unless the URL explicitly contains `#backchannel=1`, so a stream dedicated to two-way talk should carry no modifiers at all.
+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.

 :::

@@ -155,7 +153,7 @@ WebRTC is only attempted when MSE fails or when using a camera's two-way talk fe

 - **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, carrying no `#` modifiers of any kind, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
+- **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

@@ -209,50 +209,6 @@ If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parame

 </FaqItem>

-<FaqItem id="invalid-or-missing-video-stream-in-segment" question="I see the message: WARNING : Invalid or missing video stream in segment ... Discarding.">
-
-Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
-
- `Invalid or missing video stream in segment <path>. Discarding.` The segment holds no video, or could not be read at all.
- `Failed to probe corrupt segment <path>` followed by `Discarding a corrupt recording segment: <path>`. The segment was read, but its length could not be determined.
- `Discarding a corrupt recording segment: <path>` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
-
-For each one, the camera watchdog also logs `Invalid recording segment detected for <camera> at <timestamp>`.
-
-:::warning
-
-This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
-
-:::
-
-#### Start with the camera and the network
-
- **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
- **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
- **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
- **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
-
-Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from <camera> in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
-
-#### If the camera and network check out
-
- **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
- **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
- **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
-
-</FaqItem>
-
-<FaqItem id="no-new-recording-segments-were-created" question="I see the message: ERROR : No new recording segments were created for <camera> in the last 120s. Restarting the ffmpeg record process...">
-
-When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
-
- **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
- **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
-
-The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
-
-</FaqItem>
-
 <FaqItem id="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" question="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.
@@ -397,11 +353,19 @@ dmesg | grep -i -E "gpu|drm|reset|hang"

 Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.

-#### Step 6: Verify go2rtc stream configuration
+#### Step 6: Verify hardware acceleration configuration
+
+An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
+
+- 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.
+
+#### Step 7: Verify go2rtc stream configuration

 Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.

-#### Step 7: Check system resources
+#### Step 8: Check system resources

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

@@ -3,9 +3,6 @@ import * as path from "node:path";
 import type { Config, PluginConfig } from "@docusaurus/types";
 import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";

-// Bump when a new stable release ships
-const STABLE_VERSION = "0.18";
-
 const config: Config = {
  title: "Frigate",
  tagline: "NVR With Realtime Object Detection for IP Cameras",
@@ -26,17 +23,17 @@ const config: Config = {
    mermaid: true,
  },
  i18n: {
-    defaultLocale: "en",
-    locales: ["en"],
+    defaultLocale: 'en',
+    locales: ['en'],
    localeConfigs: {
      en: {
-        label: "English",
-      },
+        label: 'English',
+      }
    },
  },
  themeConfig: {
    announcementBar: {
-      id: "frigate_plus",
+      id: 'frigate_plus',
      content: `
        <span style="margin-right: 8px; display: inline-block; animation: pulse 2s infinite;">🚀</span>
        Get more relevant and accurate detections with Frigate+ models.
@@ -48,8 +45,8 @@ const config: Config = {
            50%       { transform: scale(1.1); }
          }
        </style>`,
-      backgroundColor: "#005f73",
-      textColor: "#e0fbfc",
+      backgroundColor: '#005f73',
+      textColor: '#e0fbfc',
      isCloseable: false,
    },
    docs: {
@@ -86,15 +83,15 @@ const config: Config = {
      },
    },
    prism: {
-      magicComments: [
+      magicComments:[
        {
-          className: "theme-code-block-highlighted-line",
-          line: "highlight-next-line",
-          block: { start: "highlight-start", end: "highlight-end" },
+          className: 'theme-code-block-highlighted-line',
+          line: 'highlight-next-line',
+          block: {start: 'highlight-start', end: 'highlight-end'},
        },
        {
-          className: "code-block-error-line",
-          line: "highlight-error-line",
+          className: 'code-block-error-line',
+          line: 'highlight-error-line',
        },
      ],
      additionalLanguages: ["bash", "json"],
@@ -134,11 +131,6 @@ const config: Config = {
        srcDark: "img/branding/logo-dark.svg",
      },
      items: [
-        {
-          href: "https://github.com/blakeblackshear/frigate/releases",
-          label: `${STABLE_VERSION}`,
-          position: "left",
-        },
        {
          to: "/",
          activeBasePath: "docs",
@@ -156,19 +148,19 @@ const config: Config = {
          position: "right",
        },
        {
-          type: "localeDropdown",
-          position: "right",
+          type: 'localeDropdown',
+          position: 'right',
          dropdownItemsAfter: [
            {
-              label: "简体中文（社区翻译）",
-              href: "https://docs.frigate-cn.video",
-            },
-          ],
+              label: '简体中文（社区翻译）',
+              href: 'https://docs.frigate-cn.video',
+            }
+          ]
        },
        {
-          href: "https://github.com/blakeblackshear/frigate",
-          label: "GitHub",
-          position: "right",
+          href: 'https://github.com/blakeblackshear/frigate',
+          label: 'GitHub',
+          position: 'right',
        },
      ],
    },
@@ -2,53 +2,42 @@ import React, { useState, useEffect } from "react";
 import Admonition from "@theme/Admonition";
 import styles from "./styles.module.css";

-// follows calculate_shm_requirements() in frigate/util/services.py, but sizes
-// for 25 frames so float rounding can't land under its 20 frame warning
-const MB = 1048576;
-const RESERVED_MB = 50; // logs + nginx cache
-const TARGET_FRAMES = 25;
-const DEFAULT_FRAMES = 50;
-
-const frameMb = (width, height) =>
-  Math.round(((width * height * 1.5 + 270480) / MB) * 10) / 10;
-
-// room for 2 cameras added at runtime, sized as 720p
-const addedCamerasMb = 2 * frameMb(1280, 720);
-
 const ShmCalculator = () => {
  const [width, setWidth] = useState(1280);
  const [height, setHeight] = useState(720);
  const [cameraCount, setCameraCount] = useState(1);
-  const [result, setResult] = useState("-");
-  const [singleCameraShm, setSingleCameraShm] = useState("-");
-  const [addedCamerasShm, setAddedCamerasShm] = useState("-");
-  const [fullRingShm, setFullRingShm] = useState("-");
+  const [result, setResult] = useState("26.32MB");
+  const [singleCameraShm, setSingleCameraShm] = useState("26.32MB");
+  const [totalShm, setTotalShm] = useState("26.32MB");

  const calculate = () => {
    if (!width || !height || !cameraCount) {
      setResult("Please enter valid values");
      setSingleCameraShm("-");
-      setAddedCamerasShm("-");
-      setFullRingShm("-");
+      setTotalShm("-");
      return;
    }

-    const cameraFramesMb = cameraCount * frameMb(width, height) + addedCamerasMb;
+    // Single camera base SHM calculation (excluding logs)
+    // Formula: (width * height * 1.5 * 20 + 270480) / 1048576
+    const singleCameraBase =
+      (width * height * 1.5 * 20 + 270480) / 1048576;
+    setSingleCameraShm(`${singleCameraBase.toFixed(2)}mb`);

-    setSingleCameraShm(formatMb(frameMb(width, height) * TARGET_FRAMES));
-    setAddedCamerasShm(formatMb(addedCamerasMb * TARGET_FRAMES));
-    setResult(formatMb(Math.round(RESERVED_MB + cameraFramesMb * TARGET_FRAMES)));
-    setFullRingShm(
-      formatMb(Math.round(RESERVED_MB + cameraFramesMb * DEFAULT_FRAMES)),
-    );
-  };
+    // Total SHM calculation (multiple cameras, including logs)
+    const totalBase = singleCameraBase * cameraCount;
+    const finalResult = totalBase + 40; // Default includes logs +40mb

-  const formatMb = (value) => {
-    if (value >= 1024) {
-      return `${(value / 1024).toFixed(2)}gb`;
+    setTotalShm(`${(totalBase + 40).toFixed(2)}mb`);
+
+    // Format result
+    if (finalResult < 1) {
+      setResult(`${(finalResult * 1024).toFixed(2)}kb`);
+    } else if (finalResult >= 1024) {
+      setResult(`${(finalResult / 1024).toFixed(2)}gb`);
+    } else {
+      setResult(`${finalResult.toFixed(2)}mb`);
    }
-
-    return `${Number(value.toFixed(2))}mb`;
  };

  const formatWithUnit = (value) => {
@@ -158,27 +147,19 @@ const ShmCalculator = () => {
          </div>
          <div className={styles.formulaDisplay}>
            <p>
-              <strong>Per Camera:</strong> {formatWithUnit(singleCameraShm)}
+              <strong>Single Camera:</strong> {formatWithUnit(singleCameraShm)}
            </p>
            <p>
-              <strong>Formula:</strong> (width × height × 1.5 + 270480) ÷
-              1048576 × {TARGET_FRAMES} frames
+              <strong>Formula:</strong> (width × height × 1.5 × 20 + 270480) ÷
+              1048576
            </p>
+            {cameraCount > 1 && (
+              <p>
+                <strong>Total ({cameraCount} cameras):</strong> {formatWithUnit(totalShm)}
+              </p>
+            )}
            <p>
-              <strong>Room for 2 Added Cameras:</strong>{" "}
-              {formatWithUnit(addedCamerasShm)}
-            </p>
-            <p>
-              <strong>Logs and Cache:</strong> + {RESERVED_MB}
-              <span className={styles.unit}>mb</span>
-            </p>
-            <p>
-              <strong>Birdseye Restream:</strong> + 8
-              <span className={styles.unit}>mb</span> if enabled
-            </p>
-            <p>
-              <strong>For {DEFAULT_FRAMES} Frames:</strong>{" "}
-              {formatWithUnit(fullRingShm)}
+              <strong>With Logs:</strong> + 40<span className={styles.unit}>mb</span>
            </p>
          </div>
        </div>
@@ -1476,7 +1476,7 @@ paths:
        - Classification
      summary: Get custom classification attributes
      description: |-
-        **Access:** Authenticated user with access to all cameras.
+        **Access:** Admin role required.

        Returns custom classification attributes for a given object type.
            Only includes models with classification_type set to 'attribute'.
@@ -1510,8 +1510,8 @@ paths:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
-        - frigateUserAuth: []
-      x-required-role: all_cameras
+        - frigateAdminAuth: []
+      x-required-role: admin
  /classification/{name}/train:
    get:
      tags:
@@ -2308,15 +2308,15 @@ paths:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateUserAuth: []
-      x-required-role: camera
-      description: '**Access:** Authenticated user with access to the referenced camera.'
+      x-required-role: any
+      description: '**Access:** Any authenticated user.'
  /review/summarize/start/{start_ts}/end/{end_ts}:
    post:
      tags:
        - Review
      summary: Generate Review Summary
      description: |-
-        **Access:** Authenticated user with access to all cameras.
+        **Access:** Admin role required.

        Use GenAI to summarize review items over a period of time.
      operationId: 
@@ -2347,8 +2347,8 @@ paths:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
-        - frigateUserAuth: []
-      x-required-role: all_cameras
+        - frigateAdminAuth: []
+      x-required-role: admin
  /:
    get:
      tags:
@@ -2946,6 +2946,44 @@ paths:
        - frigateUserAuth: []
      x-required-role: any
      description: '**Access:** Any authenticated user.'
+  /categorized_object_names:
+    get:
+      tags:
+        - App
+      summary: Get known object names by object type
+      description: |-
+        **Access:** Any authenticated user.
+
+        Returns the sub labels and attributes this install can attach,
+            grouped by object type. Unlike /sub_labels, which reflects what has already been
+            detected, this reads the config and model files, so it covers recognized face
+            names, named license plates, custom object classification categories, and the
+            detector attributes of tracked objects.
+      operationId: categorized_object_names_categorized_object_names_get
+      parameters:
+        - name: object_type
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: string
+              - type: 'null'
+            title: Object Type
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateUserAuth: []
+      x-required-role: any
  /audio_labels:
    get:
      tags:
@@ -4073,16 +4111,6 @@ paths:
              - type: 'null'
            default: 100
            title: Limit
-        - name: offset
-          in: query
-          required: false
-          schema:
-            anyOf:
-              - type: integer
-                minimum: 0
-              - type: 'null'
-            default: 0
-            title: Offset
        - name: after
          in: query
          required: false
@@ -4388,16 +4416,6 @@ paths:
              - type: 'null'
            default: 50
            title: Limit
-        - name: offset
-          in: query
-          required: false
-          schema:
-            anyOf:
-              - type: integer
-                minimum: 0
-              - type: 'null'
-            default: 0
-            title: Offset
        - name: cameras
          in: query
          required: false
@@ -7129,9 +7147,7 @@ paths:
              schema:
                $ref: '#/components/schemas/DebugReplayStartResponse'
        '400':
-          description: Invalid camera or time range
-        '404':
-          description: No recordings in the requested time range
+          description: Invalid camera, time range, or no recordings
        '409':
          description: A replay session is already active
        '422':
@@ -15,9 +15,7 @@ from frigate.util.config import find_config_file


 def main() -> None:
-    # Fork this manager so the forkserver does not start until the first
-    # worker process, after the config has applied environment_vars.
-    manager = mp.get_context("fork").Manager()
+    manager = mp.Manager()
    faulthandler.enable()

    # Setup the logging thread
@@ -71,10 +71,9 @@ from frigate.util.config import (
    find_config_file,
    redact_credential,
 )
+from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
-    Go2rtcStreamIssue,
-    find_restricted_go2rtc_stream_changes,
    get_nvidia_driver_info,
    process_logs,
    restart_frigate,
@@ -293,10 +292,6 @@ def config(request: Request):
    config: dict[str, dict[str, Any]] = config_obj.model_dump(
        mode="json", warnings="none", exclude_none=True
    )
-    config["detectors"] = {
-        name: detector.model_dump(mode="json", warnings="none", exclude_none=True)
-        for name, detector in config_obj.detectors.items()
-    }

    # remove environment_vars for non-admin users
    if request.headers.get("remote-role") != "admin":
@@ -377,31 +372,28 @@ def config(request: Request):
        config["go2rtc"]["streams"][stream_name] = cleaned

    config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
-    config["model"]["colormap"] = config_obj.model.colormap
-    config["model"]["all_attributes"] = config_obj.model.all_attributes
-    config["model"]["non_logo_attributes"] = config_obj.model.non_logo_attributes

-    # Add model plus data if plus is enabled
-    if config["plus"]["enabled"]:
-        model_path = config.get("model", {}).get("path")
-        if model_path:
-            model_json_path = FilePath(model_path).with_suffix(".json")
+    for index, model in enumerate(config_obj.models):
+        model_dict = config["models"][index]
+        model_dict["colormap"] = model.colormap
+        model_dict["all_attributes"] = model.all_attributes
+        model_dict["non_logo_attributes"] = model.non_logo_attributes
+        model_dict["labelmap"] = model.merged_labelmap
+
+        if not config["plus"]["enabled"]:
+            continue
+
+        # Add model plus data if plus is enabled
+        model_dict["plus"] = None
+
+        if model.path:
+            model_json_path = FilePath(model.path).with_suffix(".json")
+
            try:
                with open(model_json_path) as f:
-                    model_plus_data = json.load(f)
-                config["model"]["plus"] = model_plus_data
-            except FileNotFoundError:
-                config["model"]["plus"] = None
-            except json.JSONDecodeError:
-                config["model"]["plus"] = None
-        else:
-            config["model"]["plus"] = None
-
-    # use merged labelamp
-    for detector_config in config["detectors"].values():
-        detector_config["model"]["labelmap"] = (
-            request.app.frigate_config.model.merged_labelmap
-        )
+                    model_dict["plus"] = json.load(f)
+            except (FileNotFoundError, json.JSONDecodeError):
+                pass

    return JSONResponse(content=config)

@@ -524,63 +516,6 @@ def config_raw():
        )


-def _go2rtc_streams(raw_config: str) -> Any:
-    """Load go2rtc.streams with the same loader Frigate and go2rtc startup use.
-
-    Raises ruamel.yaml.YAMLError when the config cannot be parsed.
-    """
-    loaded = ruamel.yaml.YAML().load(raw_config)
-    go2rtc = loaded.get("go2rtc") if isinstance(loaded, dict) else None
-    return go2rtc.get("streams") if isinstance(go2rtc, dict) else None
-
-
-def _restricted_go2rtc_streams_response(
-    old_raw_config: str, new_raw_config: str
-) -> JSONResponse | None:
-    """Reject config changes that add restricted go2rtc stream sources.
-
-    The same sources are rejected by PUT /go2rtc/streams, and go2rtc would
-    otherwise load them from the saved config on the next restart.
-    """
-    try:
-        new_streams = _go2rtc_streams(new_raw_config)
-    except ruamel.yaml.YAMLError:
-        logger.warning("Rejected config change that could not be parsed")
-        return JSONResponse(
-            content={"success": False, "message": "Unable to parse config"},
-            status_code=400,
-        )
-
-    try:
-        old_streams = _go2rtc_streams(old_raw_config)
-    except ruamel.yaml.YAMLError:
-        # with no readable saved config, every source is treated as new
-        old_streams = None
-
-    issues = find_restricted_go2rtc_stream_changes(old_streams, new_streams)
-    if not issues:
-        return None
-
-    messages = []
-    for issue, label in (
-        (Go2rtcStreamIssue.restricted, "Restricted go2rtc stream source in"),
-        (Go2rtcStreamIssue.unknown_variable, "Unknown variable in go2rtc stream"),
-        (
-            Go2rtcStreamIssue.invalid_placeholder,
-            "Invalid variable placeholder in go2rtc stream",
-        ),
-    ):
-        names = [name for name, found in issues.items() if found == issue]
-        if names:
-            messages.append(f"{label}: {', '.join(names)}")
-
-    logger.warning("Rejected config change: %s", "; ".join(messages))
-    return JSONResponse(
-        content={"success": False, "message": "; ".join(messages)},
-        status_code=400,
-    )
-
-
@router.post("/config/save", dependencies=[Depends(require_role(["admin"]))])
 def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
    new_config = body.decode()
@@ -657,22 +592,10 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
            status_code=400,
        )

-    config_file = find_config_file()
-
-    try:
-        with open(config_file) as f:
-            old_raw_config = f.read()
-    except OSError:
-        old_raw_config = ""
-
-    restricted_response = _restricted_go2rtc_streams_response(
-        old_raw_config, new_config
-    )
-    if restricted_response is not None:
-        return restricted_response
-
    # Save the config to file
    try:
+        config_file = find_config_file()
+
        with open(config_file, "w") as f:
            f.write(new_config)
            f.close()
@@ -983,13 +906,6 @@ def config_set(request: Request, body: AppConfigSetBody):
                        ),
                        status_code=400,
                    )
-                restricted_response = _restricted_go2rtc_streams_response(
-                    old_raw_config, new_raw_config
-                )
-                if restricted_response is not None:
-                    with open(config_file, "w") as f:
-                        f.write(old_raw_config)
-                    return restricted_response
            except Exception as e:
                logging.error(f"Error updating config: {e}")
                return JSONResponse(
@@ -1391,6 +1307,28 @@ def get_sub_labels(
    return JSONResponse(content=sub_labels)


+@router.get(
+    "/categorized_object_names",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get known object names by object type",
+    description="""Returns the sub labels and attributes this install can attach,
+    grouped by object type. Unlike /sub_labels, which reflects what has already been
+    detected, this reads the config and model files, so it covers recognized face
+    names, named license plates, custom object classification categories, and the
+    detector attributes of tracked objects.""",
+)
+def categorized_object_names(
+    request: Request,
+    object_type: str | None = None,
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+):
+    return JSONResponse(
+        content=get_categorized_object_names(
+            request.app.frigate_config, allowed_cameras, object_type
+        )
+    )
+
+
@router.get("/audio_labels", dependencies=[Depends(allow_any_authenticated())])
 def get_audio_labels():
    labels = load_labels("/audio-labelmap.txt", prefill=521)
@@ -1415,11 +1353,14 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):

    modelList = models["list"]

+    config: FrigateConfig = request.app.frigate_config
+    primary_model = config.primary_model
+
    # current model type
-    modelType = request.app.frigate_config.model.model_type
+    modelType = primary_model.model_type

    # current detectorType for comparing to supportedDetectors
-    detectorType = list(request.app.frigate_config.detectors.values())[0].type
+    detectorType = config.devices_for_model(primary_model)[0].detector

    validModels = []

@@ -11,9 +11,8 @@ import secrets
 import time
 from datetime import datetime
 from pathlib import Path
-from urllib.parse import parse_qs, unquote
+from urllib.parse import parse_qs, urlparse

-import requests
 from fastapi import APIRouter, Depends, HTTPException, Request, Response
 from fastapi.responses import JSONResponse, RedirectResponse
 from joserfc import jwt
@@ -84,9 +83,9 @@ def require_admin_by_default():
        "/nvinfo",
        "/labels",
        "/sub_labels",
+        "/categorized_object_names",
        "/plus/models",
        "/recognized_license_plates",
-        "/classification/attributes",
        "/timeline",
        "/timeline/hourly",
        "/recordings/storage",
@@ -128,18 +127,23 @@ def require_admin_by_default():
        if path.startswith(EXEMPT_PREFIXES):
            return

-        # Camera routes enforce per-camera access via route-level dependencies
-        # (e.g. require_camera_access). Match on the route template, not the raw
-        # path, so a camera named like another namespace (e.g. "faces") can't
-        # waive the admin check for that namespace's routes.
-        route = request.scope.get("route")
-        if (
-            route is not None
-            and route.path.startswith("/{camera_name}")
-            and request.path_params.get("camera_name")
-            in request.app.frigate_config.cameras
-        ):
-            return
+        # Dynamic camera path exemption:
+        # Any path whose first segment matches a configured camera name should
+        # bypass the global admin requirement. These endpoints enforce access
+        # via route-level dependencies (e.g. require_camera_access) to ensure
+        # per-camera authorization. This allows non-admin authenticated users
+        # (e.g. viewer role) to access camera-specific resources without
+        # needing admin privileges.
+        try:
+            if path.startswith("/"):
+                first_segment = path.split("/", 2)[1]
+                if (
+                    first_segment
+                    and first_segment in request.app.frigate_config.cameras
+                ):
+                    return
+        except Exception:
+            pass

        # For all other paths, require admin role
        # Internal port requests have admin role set automatically
@@ -913,7 +917,7 @@ def create_user(
    HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
    config_roles = list(request.app.frigate_config.auth.roles.keys())

-    if not re.match(r"^(?!\.+$)[A-Za-z0-9._]+$", body.username):
+    if not re.match("^[A-Za-z0-9._]+$", body.username):
        return JSONResponse(content={"message": "Invalid username"}, status_code=400)

    if body.role not in config_roles:
@@ -968,7 +972,6 @@ def delete_user(request: Request, username: str):
    summary="Update user password",
    description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity for non-admin users. Password must be at least 12 characters long. If user changes their own password, a new JWT cookie is automatically issued.",
 )
-@limiter.limit(limit_value=rateLimiter.get_limit)
 async def update_password(
    request: Request,
    username: str,
@@ -982,11 +985,10 @@ async def update_password(
    current_username = current_user.get("username")
    current_role = current_user.get("role")

-    # Only admins may target another account. This has to cover every non-admin
-    # role rather than just viewer, since custom roles are arbitrary names
-    if current_role != "admin" and current_username != username:
+    # viewers can only change their own password
+    if current_role == "viewer" and current_username != username:
        raise HTTPException(
-            status_code=403, detail="Users can only update their own password"
+            status_code=403, detail="Viewers can only update their own password"
        )

    HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
@@ -1139,44 +1141,11 @@ def _get_stream_owner_cameras(request: Request, stream_name: str) -> set[str]:
    return owner_cameras


-def _is_existing_go2rtc_stream(request: Request, stream_name: str) -> bool:
-    """Check that `stream_name` names a stream go2rtc already knows about.
-
-    go2rtc turns any `src` that is not an existing stream name into a new ad hoc
-    stream and starts the producer it describes, so the proxied live-stream
-    paths must only ever reference existing streams.
-    """
-    frigate_config = request.app.frigate_config
-
-    if _get_stream_owner_cameras(request, stream_name):
-        return True
-
-    config_streams = getattr(frigate_config.go2rtc, "streams", None) or {}
-    if stream_name in config_streams:
-        return True
-
-    # streams added at runtime through PUT /go2rtc/streams only exist in go2rtc
-    try:
-        r = requests.get("http://127.0.0.1:1984/api/streams", timeout=2)
-    except requests.RequestException:
-        logger.warning("Unable to fetch streams from go2rtc to authorize request")
-        return False
-
-    if not r.ok:
-        return False
-
-    try:
-        return stream_name in r.json()
-    except ValueError:
-        return False
-
-
 # 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. The nginx locations are prefix
-# matches, so anything under these paths is proxied too.
+# endpoint, not these proxied live-stream paths.
 GO2RTC_STREAM_PROXY_PATHS = frozenset(
    {
        "/live/mse/api/ws",
@@ -1185,80 +1154,32 @@ GO2RTC_STREAM_PROXY_PATHS = frozenset(
    }
 )

-_URI_SCHEME_AND_AUTHORITY = re.compile(r"^[A-Za-z][A-Za-z0-9+.-]*://[^/?]*")
-
-
-def _split_request_uri(request_uri: str) -> tuple[str, str]:
-    """Split a raw request URI into its path and query string.
-
-    urlparse is not used since it reads a path starting with `//` as a host.
-    """
-    uri = _URI_SCHEME_AND_AUTHORITY.sub("", request_uri, count=1)
-    path, _, query = uri.partition("?")
-    return path, query
-
-
-def _normalize_request_path(path: str) -> str:
-    """Normalize a raw request path the way nginx does before matching locations.
-
-    nginx percent-decodes the path, merges repeated slashes and resolves dot
-    segments, while `X-Original-URL` carries the raw `$request_uri`.
-    """
-    segments: list[str] = []
-    for segment in unquote(path).split("/"):
-        if segment in ("", "."):
-            continue
-        if segment == "..":
-            if segments:
-                segments.pop()
-            continue
-        segments.append(segment)
-
-    return "/" + "/".join(segments)
-
-
-def _is_go2rtc_stream_proxy_request(path: str, request: Request) -> bool:
-    # nginx marks requests routed through the go2rtc stream locations, and the
-    # normalized path catches them if that marker is ever missing
-    if request.headers.get("x-go2rtc-stream-proxy") == "1":
-        return True
-
-    normalized = _normalize_request_path(path)
-    return any(normalized.startswith(p) for p in GO2RTC_STREAM_PROXY_PATHS)
-

 def deny_response_for_go2rtc_stream(
    original_url: str | None, role: str | None, request: Request
 ) -> int | None:
-    """Block go2rtc live-stream requests the current role may not make.
+    """Block role-restricted users from go2rtc live streams they cannot access.

-    Returns 403 when any `src` named in `original_url` is not an existing go2rtc
-    stream (for every role), or when it resolves to a camera outside a
-    restricted role's allow-list (or no `src` is provided for such a role),
-    otherwise None. Mirrors the resolution logic in
+    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

-    path, query = _split_request_uri(original_url)
-    if not _is_go2rtc_stream_proxy_request(path, request):
+    parsed = urlparse(original_url)
+    if parsed.path not in GO2RTC_STREAM_PROXY_PATHS:
        return None

    frigate_config = request.app.frigate_config
-    sources = parse_qs(query, keep_blank_values=True).get("src", [])
-
-    # an unknown src would make go2rtc create an ad hoc stream from it, which
-    # bypasses the restricted-source checks on PUT /go2rtc/streams
-    for src in sources:
-        if not _is_existing_go2rtc_stream(request, src):
-            return 403

    # 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
@@ -1331,23 +1252,3 @@ async def get_allowed_cameras_for_filter(request: Request):
    all_camera_names = set(request.app.frigate_config.cameras.keys())
    roles_dict = request.app.frigate_config.auth.roles
    return User.get_allowed_cameras(role, roles_dict, all_camera_names)
-
-
-async def require_full_camera_access(
-    request: Request,
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
-):
-    """Dependency for endpoints returning data that spans every camera.
-
-    Some responses cannot be meaningfully scoped to a subset of cameras, so
-    rather than filter them the endpoint is limited to callers who can already
-    see every camera. Admin and viewer always qualify; a custom role qualifies
-    only when its camera list covers all configured cameras.
-    """
-    all_camera_names = set(request.app.frigate_config.cameras.keys())
-
-    if not all_camera_names.issubset(allowed_cameras):
-        raise HTTPException(
-            status_code=403,
-            detail="Access to all cameras is required for this endpoint",
-        )
@@ -35,18 +35,14 @@ 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,
-    clear_orphaned_comments,
-    get_record_segment_time,
-)
+from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
 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_api_source,
+    is_restricted_go2rtc_source,
 )

 logger = logging.getLogger(__name__)
@@ -152,9 +148,9 @@ 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_api_source(src):
+    if src and is_restricted_go2rtc_source(src):
        logger.warning(
-            "Rejected go2rtc stream '%s' with restricted source",
+            "Rejected go2rtc stream '%s' with restricted source type (echo/expr/exec)",
            stream_name,
        )
        return JSONResponse(
@@ -173,9 +169,9 @@ def go2rtc_add_stream(request: Request, stream_name: str, src: str = ""):
            except KeyError:
                resolved_src = src

-            if is_restricted_go2rtc_api_source(resolved_src):
+            if is_restricted_go2rtc_source(resolved_src):
                logger.warning(
-                    "Rejected go2rtc stream '%s' with restricted source",
+                    "Rejected go2rtc stream '%s' with restricted source type (echo/expr/exec)",
                    stream_name,
                )
                return JSONResponse(
@@ -1205,7 +1201,6 @@ async def delete_camera(
                # Remove camera from config
                if "cameras" in data and camera_name in data["cameras"]:
                    del data["cameras"][camera_name]
-                    clear_orphaned_comments(data["cameras"], data, "cameras")

                # Remove camera from auth roles
                auth = data.get("auth", {})
@@ -50,6 +50,7 @@ from frigate.jobs.vlm_watch import (
    stop_vlm_watch_job,
 )
 from frigate.models import Event
+from frigate.util.object_names import get_categorized_object_names

 logger = logging.getLogger(__name__)

@@ -539,6 +540,11 @@ async def execute_tool(
    if tool_name == "search_objects":
        return await _execute_search_objects(request, arguments, allowed_cameras)

+    if tool_name == "get_categorized_object_names":
+        return JSONResponse(
+            content=_execute_get_categorized_object_names(request, allowed_cameras)
+        )
+
    if tool_name == "find_similar_objects":
        result = await _execute_find_similar_objects(
            request, arguments, allowed_cameras
@@ -717,6 +723,21 @@ async def _execute_set_camera_state(
    return {"success": True, "camera": camera, "feature": feature, "value": value}


+def _execute_get_categorized_object_names(
+    request: Request,
+    allowed_cameras: list[str],
+) -> dict[str, Any]:
+    names = get_categorized_object_names(request.app.frigate_config, allowed_cameras)
+
+    if not names:
+        return {
+            "names": {},
+            "message": "No names configured; search by label or semantic_query.",
+        }
+
+    return {"names": names}
+
+
 async def _execute_tool_internal(
    tool_name: str,
    arguments: dict[str, Any],
@@ -741,6 +762,8 @@ async def _execute_tool_internal(
        except (json.JSONDecodeError, AttributeError) as e:
            logger.warning(f"Failed to extract tool result: {e}")
            return {"error": "Failed to parse tool result"}
+    elif tool_name == "get_categorized_object_names":
+        return _execute_get_categorized_object_names(request, allowed_cameras)
    elif tool_name == "find_similar_objects":
        return await _execute_find_similar_objects(request, arguments, allowed_cameras)
    elif tool_name == "set_camera_state":
@@ -773,8 +796,8 @@ async def _execute_tool_internal(
    else:
        logger.error(
            "Tool call failed: unknown tool %r. Expected one of: search_objects, find_similar_objects, "
-            "get_live_context, start_camera_watch, stop_camera_watch, get_profile_status, get_recap. "
-            "Arguments received: %s",
+            "get_categorized_object_names, get_live_context, start_camera_watch, stop_camera_watch, "
+            "get_profile_status, get_recap. Arguments received: %s",
            tool_name,
            json.dumps(arguments),
        )
@@ -14,7 +14,7 @@ from fastapi.responses import JSONResponse
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict

-from frigate.api.auth import require_full_camera_access, require_role
+from frigate.api.auth import require_role
 from frigate.api.defs.request.classification_body import (
    AudioTranscriptionBody,
    DeleteFaceImagesBody,
@@ -741,7 +741,6 @@ def get_classification_dataset(name: str):

@router.get(
    "/classification/attributes",
-    dependencies=[Depends(require_full_camera_access)],
    summary="Get custom classification attributes",
    description="""Returns custom classification attributes for a given object type.
    Only includes models with classification_type set to 'attribute'.
@@ -13,7 +13,6 @@ from frigate.api.auth import require_role
 from frigate.api.defs.tags import Tags
 from frigate.jobs.debug_replay import (
    ExportDebugReplaySource,
-    NoRecordingsError,
    RecordingDebugReplaySource,
    start_debug_replay_job,
 )
@@ -75,8 +74,7 @@ class DebugReplayStopResponse(BaseModel):
    response_model=DebugReplayStartResponse,
    status_code=202,
    responses={
-        400: {"description": "Invalid camera or time range"},
-        404: {"description": "No recordings in the requested time range"},
+        400: {"description": "Invalid camera, time range, or no recordings"},
        409: {"description": "A replay session is already active"},
    },
    dependencies=[Depends(require_role(["admin"]))],
@@ -115,14 +113,6 @@ async def start_debug_replay(request: Request, body: DebugReplayStartBody):
            },
            status_code=409,
        )
-    except NoRecordingsError:
-        return JSONResponse(
-            content={
-                "success": False,
-                "message": "No recordings found in the selected time range",
-            },
-            status_code=404,
-        )
    except ValueError:
        logger.exception("Rejected debug replay start request")
        return JSONResponse(
@@ -14,7 +14,6 @@ class EventsQueryParams(BaseModel):
    zone: str | None = "all"
    zones: str | None = "all"
    limit: int | None = 100
-    offset: int | None = Field(0, ge=0)
    after: float | None = None
    before: float | None = None
    time_range: str | None = DEFAULT_TIME_RANGE
@@ -56,7 +55,6 @@ class EventsSearchQueryParams(BaseModel):
        deprecated=True,
    )
    limit: int | None = 50
-    offset: int | None = Field(0, ge=0)
    cameras: str | None = "all"
    labels: str | None = "all"
    sub_labels: str | None = "all"
@@ -129,7 +129,6 @@ def events(
        zones = zone

    limit = params.limit
-    offset = params.offset
    after = params.after
    before = params.before
    time_range = params.time_range
@@ -362,15 +361,11 @@ def events(
    else:
        order_by = Event.start_time.desc()

-    # offset paging needs a stable order when scores or speeds tie
-    tiebreaker = [Event.id] if sort and sort.startswith(("score", "speed")) else []
-
    events = (
        Event.select(*selected_columns)
        .where(reduce(operator.and_, clauses))
-        .order_by(order_by, *tiebreaker)
+        .order_by(order_by)
        .limit(limit)
-        .offset(offset)
        .dicts()
        .iterator()
    )
@@ -523,7 +518,6 @@ def events_search(
    search_type = params.search_type
    include_thumbnails = params.include_thumbnails
    limit = params.limit
-    offset = params.offset
    sort = params.sort

    # Filters
@@ -830,9 +824,6 @@ def events_search(
    if search_results:
        events_query = events_query.where(Event.id << list(search_results.keys()))

-    # sorts below are stable, so this orders ties for offset paging
-    events_query = events_query.order_by(Event.id)
-
    # Fetch events and process them in a single pass
    processed_events = []
    for event in events_query.dicts():
@@ -890,7 +881,7 @@ def events_search(
        processed_events.sort(key=lambda x: x["start_time"], reverse=True)

    # Limit the number of events returned
-    processed_events = processed_events[offset:][:limit]
+    processed_events = processed_events[:limit]

    return JSONResponse(content=processed_events)

@@ -2,7 +2,6 @@

 import datetime
 import logging
-import os
 import random
 import string
 import time
@@ -10,7 +9,6 @@ import zipfile
 from collections import deque
 from collections.abc import Iterator
 from pathlib import Path
-from urllib.parse import quote

 import psutil
 from fastapi import APIRouter, Depends, Query, Request
@@ -57,7 +55,6 @@ from frigate.api.defs.response.export_response import (
 )
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
-from frigate.api.media_auth import deny_response_for_media_uri
 from frigate.const import CLIPS_DIR, EXPORT_DIR
 from frigate.jobs.export import (
    ExportJob,
@@ -71,7 +68,6 @@ from frigate.jobs.export import (
 from frigate.models import Export, ExportCase, Previews, Recordings
 from frigate.record.export import (
    DEFAULT_TIME_LAPSE_FFMPEG_ARGS,
-    DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS,
    ChaptersEnum,
    PlaybackSourceEnum,
    validate_ffmpeg_args,
@@ -132,7 +128,6 @@ def _validate_export_case(export_case_id: str | None) -> JSONResponse | None:


 def _sanitize_existing_image(
-    request: Request,
    image_path: str | None,
 ) -> tuple[str | None, JSONResponse | None]:
    if not image_path:
@@ -140,15 +135,6 @@ def _sanitize_existing_image(

    existing_image = sanitize_contained_path(image_path, CLIPS_DIR)

-    # CLIPS_DIR is shared by every camera, so the caller must also be allowed
-    # to read the image.
-    if existing_image is not None and deny_response_for_media_uri(
-        f"/clips/{quote(os.path.relpath(existing_image, CLIPS_DIR))}",
-        request.headers.get("remote-role"),
-        request.app.frigate_config,
-    ):
-        existing_image = None
-
    if existing_image is None:
        return None, JSONResponse(
            content={"success": False, "message": "Invalid image path"},
@@ -467,22 +453,6 @@ def _stream_case_archive(exports: list[Export]) -> Iterator[bytes]:
    yield from buffer.drain()


-def _content_disposition(filename: str, ascii_fallback: str) -> str:
-    """Build an attachment Content-Disposition that survives non-ASCII names.
-
-    Header values are encoded as latin-1, so a name outside that range cannot
-    go in filename at all. RFC 6266 handles this with a pair: a plain ASCII
-    filename for old clients, plus a percent-encoded UTF-8 filename* that
-    every current browser prefers.
-    """
-    ascii_name = filename if filename.isascii() else ascii_fallback
-
-    return (
-        f'attachment; filename="{ascii_name}"; '
-        f"filename*=UTF-8''{quote(filename, safe='')}"
-    )
-
-
@router.get(
    "/cases/{case_id}/download",
    dependencies=[Depends(allow_any_authenticated())],
@@ -525,9 +495,7 @@ def download_export_case(
        _stream_case_archive(exports),
        media_type="application/zip",
        headers={
-            "Content-Disposition": _content_disposition(
-                f"{archive_base}.zip", f"{case_id}.zip"
-            ),
+            "Content-Disposition": f'attachment; filename="{archive_base}.zip"',
        },
    )

@@ -692,7 +660,7 @@ def export_recordings_batch(
    sanitized_images: list[str | None] = []
    for item in body.items:
        existing_image, image_validation_error = _sanitize_existing_image(
-            request, item.image_path
+            item.image_path
        )
        if image_validation_error is not None:
            return image_validation_error
@@ -839,9 +807,7 @@ def export_recording(

    playback_source = body.source
    friendly_name = body.name
-    existing_image, image_validation_error = _sanitize_existing_image(
-        request, body.image_path
-    )
+    existing_image, image_validation_error = _sanitize_existing_image(body.image_path)
    if image_validation_error is not None:
        return image_validation_error

@@ -979,9 +945,7 @@ def export_recording_custom(

    playback_source = body.source
    friendly_name = body.name
-    existing_image, image_validation_error = _sanitize_existing_image(
-        request, body.image_path
-    )
+    existing_image, image_validation_error = _sanitize_existing_image(body.image_path)
    if image_validation_error is not None:
        return image_validation_error
    ffmpeg_input_args = body.ffmpeg_input_args
@@ -1029,7 +993,7 @@ def export_recording_custom(

    # Set default values if not provided (timelapse defaults)
    if ffmpeg_input_args is None:
-        ffmpeg_input_args = DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS
+        ffmpeg_input_args = ""

    if ffmpeg_output_args is None:
        ffmpeg_output_args = DEFAULT_TIME_LAPSE_FFMPEG_ARGS
@@ -351,13 +351,8 @@ async def get_snapshot_from_recording(
        mime_type = "png" if format == "png" else "jpeg"
        config: FrigateConfig = request.app.frigate_config

-        image_data = await asyncio.to_thread(
-            get_image_from_recording,
-            config.ffmpeg,
-            recording.path,
-            time_in_segment,
-            codec,
-            height,
+        image_data = get_image_from_recording(
+            config.ffmpeg, recording.path, time_in_segment, codec, height
        )

        if not image_data:
@@ -411,12 +406,8 @@ async def submit_recording_snapshot_to_plus(
        config: FrigateConfig = request.app.frigate_config
        recording: Recordings = recording_query.get()
        time_in_segment = frame_time - recording.start_time
-        image_data = await asyncio.to_thread(
-            get_image_from_recording,
-            config.ffmpeg,
-            recording.path,
-            time_in_segment,
-            "png",
+        image_data = get_image_from_recording(
+            config.ffmpeg, recording.path, time_in_segment, "png"
        )

        if not image_data:
@@ -584,11 +575,11 @@ async def vod_ts(
            Recordings.start_time,
        )
        .where(
-            Recordings.start_time.between(start_ts, end_ts)
-            | Recordings.end_time.between(start_ts, end_ts)
-            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
+            Recordings.camera == camera_name,
+            Recordings.start_time >= start_ts - MAX_SEGMENT_DURATION,
+            Recordings.start_time <= end_ts,
+            Recordings.end_time >= start_ts,
        )
-        .where(Recordings.camera == camera_name)
        .order_by(Recordings.start_time.asc())
        .iterator()
    )
@@ -823,7 +814,7 @@ async def event_snapshot(
            timestamp_style=request.app.frigate_config.cameras[
                event.camera
            ].timestamp_style,
-            colormap=request.app.frigate_config.model.colormap,
+            colormap=request.app.frigate_config.model_for_camera(event.camera).colormap,
        )
    except DoesNotExist:
        # see if the object is currently being tracked
@@ -1,10 +1,8 @@
 """Notification apis."""

-import ipaddress
 import logging
 import os
 from typing import Any
-from urllib.parse import urlparse

 from cryptography.hazmat.primitives import serialization
 from fastapi import APIRouter, Depends, Request
@@ -21,95 +19,6 @@ logger = logging.getLogger(__name__)

 router = APIRouter(tags=[Tags.notifications])

-# Push endpoints are opaque URLs but stay well under this in practice
-MAX_ENDPOINT_LENGTH = 2048
-
-# Suffixes that only ever resolve on the local network
-INTERNAL_HOST_SUFFIXES = (".local", ".localdomain", ".internal", ".home.arpa")
-
-
-def _validate_push_endpoint(endpoint: Any) -> str | None:
-    """Return a reason the endpoint is unusable, or None when it is valid.
-
-    Subscriptions are issued by the browser vendor's push service, so a valid
-    endpoint is always a public https URL. Anything else is either a broken
-    registration or an attempt to aim the notification sender somewhere it
-    should not reach.
-    """
-    if not isinstance(endpoint, str) or not endpoint:
-        return "endpoint must be a url"
-
-    if len(endpoint) > MAX_ENDPOINT_LENGTH:
-        return "endpoint is too long"
-
-    try:
-        parsed = urlparse(endpoint)
-        port = parsed.port
-    except ValueError:
-        return "endpoint is not a valid url"
-
-    if parsed.scheme != "https":
-        return "endpoint must use https"
-
-    if parsed.username or parsed.password:
-        return "endpoint must not include credentials"
-
-    if port is not None and port != 443:
-        return "endpoint must use the default https port"
-
-    hostname = parsed.hostname
-
-    if not hostname:
-        return "endpoint must include a hostname"
-
-    try:
-        address = ipaddress.ip_address(hostname)
-    except ValueError:
-        address = None
-
-    if address is not None:
-        # A push service is never reachable at an address only this network can
-        # route, so anything non-global is a misconfiguration at best
-        if not address.is_global:
-            return "endpoint must not use a private address"
-    elif hostname == "localhost" or "." not in hostname:
-        return "endpoint must use a fully qualified hostname"
-    elif hostname.endswith(INTERNAL_HOST_SUFFIXES):
-        return "endpoint must not use an internal hostname"
-
-    # The subscription token lives in the path, and webpush.py assumes there is
-    # a separator after the host when it builds the VAPID audience
-    if len(parsed.path) <= 1:
-        return "endpoint must include a subscription path"
-
-    return None
-
-
-def _validate_subscription(sub: Any) -> str | None:
-    """Return a reason the subscription is unusable, or None when it is valid."""
-    if not isinstance(sub, dict):
-        return "subscription must be an object"
-
-    reason = _validate_push_endpoint(sub.get("endpoint"))
-
-    if reason:
-        return reason
-
-    keys = sub.get("keys")
-
-    if not isinstance(keys, dict):
-        return "subscription must include keys"
-
-    # WebPusher raises on a missing key, which would break every send for the
-    # user rather than just this registration
-    for name in ("p256dh", "auth"):
-        value = keys.get(name)
-
-        if not isinstance(value, str) or not value:
-            return f"subscription keys must include {name}"
-
-    return None
-

@router.get(
    "/notifications/pubkey",
@@ -162,17 +71,6 @@ def register_notifications(request: Request, body: dict = None):
            status_code=400,
        )

-    reason = _validate_subscription(sub)
-
-    if reason:
-        logger.warning(
-            "Rejected notification registration for %s: %s", username, reason
-        )
-        return JSONResponse(
-            content={"success": False, "message": f"Invalid subscription: {reason}"},
-            status_code=400,
-        )
-
    try:
        User.update(notification_tokens=User.notification_tokens.append(sub)).where(
            User.username == username
@@ -25,7 +25,7 @@ from frigate.api.defs.query.recordings_query_parameters import (
 )
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
-from frigate.const import RECORD_DIR
+from frigate.const import MAX_SEGMENT_DURATION, RECORD_DIR
 from frigate.models import Event, Recordings
 from frigate.util.time import get_dst_transitions

@@ -243,6 +243,7 @@ async def recordings(
        )
        .where(
            Recordings.camera == camera_name,
+            Recordings.start_time >= after - MAX_SEGMENT_DURATION,
            Recordings.end_time >= after,
            Recordings.start_time <= before,
        )
@@ -17,7 +17,6 @@ from frigate.api.auth import (
    get_allowed_cameras_for_filter,
    get_current_user,
    require_camera_access,
-    require_full_camera_access,
    require_role,
 )
 from frigate.api.defs.query.review_query_parameters import (
@@ -43,22 +42,6 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.review])


-def get_label_clause(label: str, include_audio: bool = True):
-    """Build a clause matching a label within a review segment's data.
-
-    Verified objects are stored with a `-verified` suffix (eg. `person-verified`)
-    so that variant is matched as well.
-    """
-    clause = (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*') | (
-        ReviewSegment.data["objects"].cast("text") % f'*"{label}-verified"*'
-    )
-
-    if include_audio:
-        clause |= ReviewSegment.data["audio"].cast("text") % f'*"{label}"*'
-
-    return clause
-
-
@router.get(
    "/review",
    response_model=list[ReviewSegmentResponse],
@@ -108,7 +91,10 @@ async def review(
        filtered_labels = labels.split(",")

        for label in filtered_labels:
-            label_clauses.append(get_label_clause(label))
+            label_clauses.append(
+                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
+                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
+            )
        clauses.append(reduce(operator.or_, label_clauses))

    if zones != "all":
@@ -249,7 +235,10 @@ async def review_summary(
        filtered_labels = labels.split(",")

        for label in filtered_labels:
-            label_clauses.append(get_label_clause(label))
+            label_clauses.append(
+                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
+                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
+            )
        clauses.append(reduce(operator.or_, label_clauses))
    if zones != "all":
        # use matching so segments with multiple zones
@@ -347,8 +336,9 @@ async def review_summary(
        filtered_labels = labels.split(",")

        for label in filtered_labels:
-            label_clauses.append(get_label_clause(label, include_audio=False))
-
+            label_clauses.append(
+                ReviewSegment.data["objects"].cast("text") % f'*"{label}"*'
+            )
        clauses.append(reduce(operator.or_, label_clauses))

    # Find the time range of available data
@@ -719,7 +709,6 @@ async def get_review(request: Request, review_id: str):
    dependencies=[Depends(allow_any_authenticated())],
 )
 async def set_not_reviewed(
-    request: Request,
    review_id: str,
    current_user: dict = Depends(get_current_user),
 ):
@@ -738,8 +727,6 @@ async def set_not_reviewed(
            status_code=404,
        )

-    await require_camera_access(review.camera, request=request)
-
    try:
        user_review = UserReviewStatus.get(
            UserReviewStatus.user_id == user_id,
@@ -756,12 +743,9 @@ async def set_not_reviewed(
    )


-# Intentionally not camera scoped, as the summary correlates each flagged event
-# with overlapping activity on other cameras. Restricted to callers who can
-# already see every camera, so the unscoped query discloses nothing.
@router.post(
    "/review/summarize/start/{start_ts}/end/{end_ts}",
-    dependencies=[Depends(require_full_camera_access)],
+    dependencies=[Depends(require_role(["admin"]))],
    description="Use GenAI to summarize review items over a period of time.",
 )
 def generate_review_summary(request: Request, start_ts: float, end_ts: float):
@@ -49,6 +49,8 @@ from frigate.debug_replay import (
    DebugReplayManager,
    cleanup_replay_cameras,
 )
+from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.device import build_detector_config, runner_names
 from frigate.embeddings import EmbeddingProcess, EmbeddingsContext
 from frigate.events.audio import AudioProcessor
 from frigate.events.cleanup import EventCleanup
@@ -69,6 +71,7 @@ from frigate.models import (
    User,
 )
 from frigate.object_detection.base import ObjectDetectProcess
+from frigate.object_detection.util import detection_frame_size
 from frigate.output.output import OutputProcess
 from frigate.ptz.autotrack import PtzAutoTrackerThread
 from frigate.ptz.onvif import OnvifController
@@ -98,7 +101,9 @@ class FrigateApp:
        self.metrics_manager = manager
        self.audio_process: mp.Process | None = None
        self.stop_event = stop_event
-        self.detection_queue: Queue = mp.Queue()
+        self.detection_queues: dict[SceneEnum, Queue] = {
+            model.scene: mp.Queue() for model in config.models
+        }
        self.detectors: dict[str, ObjectDetectProcess] = {}
        self.detection_shms: list[mp.shared_memory.SharedMemory] = []
        self.log_queue: Queue = mp.Queue()
@@ -343,20 +348,19 @@ class FrigateApp:
        self.dispatcher.profile_manager = self.profile_manager

    def start_detectors(self) -> None:
+        model_cameras: dict[SceneEnum, list[str]] = {
+            model.scene: [] for model in self.config.models
+        }
+
        for name in self.config.cameras.keys():
+            model = self.config.model_for_camera(name)
+            model_cameras[model.scene].append(name)
+
            try:
-                largest_frame = max(
-                    [
-                        det.model.height * det.model.width * 3
-                        if det.model is not None
-                        else 320
-                        for det in self.config.detectors.values()
-                    ]
-                )
                shm_in = UntrackedSharedMemory(
                    name=name,
                    create=True,
-                    size=largest_frame,
+                    size=detection_frame_size(model),
                )
            except FileExistsError:
                shm_in = UntrackedSharedMemory(name=name)
@@ -371,15 +375,26 @@ class FrigateApp:
            self.detection_shms.append(shm_in)
            self.detection_shms.append(shm_out)

-        for name, detector_config in self.config.detectors.items():
-            self.detectors[name] = ObjectDetectProcess(
-                name,
-                self.detection_queue,
-                list(self.config.cameras.keys()),
-                self.config,
-                detector_config,
-                self.stop_event,
-            )
+        # a device may be listed more than once to run additional inference
+        # processes on it, so names are only unique once de-duplicated
+        all_devices = [
+            device
+            for model in self.config.models
+            for device in self.config.devices_for_model(model)
+        ]
+        names = iter(runner_names(all_devices))
+
+        for model in self.config.models:
+            for device in self.config.devices_for_model(model):
+                name = next(names)
+                self.detectors[name] = ObjectDetectProcess(
+                    name,
+                    self.detection_queues[model.scene],
+                    model_cameras[model.scene],
+                    self.config,
+                    build_detector_config(device, model),
+                    self.stop_event,
+                )

    def start_ptz_autotracker(self) -> None:
        self.ptz_autotracker_thread = PtzAutoTrackerThread(
@@ -410,7 +425,7 @@ class FrigateApp:
    def start_camera_processor(self) -> None:
        self.camera_maintainer = CameraMaintainer(
            self.config,
-            self.detection_queue,
+            self.detection_queues,
            self.detected_frames_queue,
            self.camera_metrics,
            self.ptz_metrics,
@@ -674,8 +689,10 @@ class FrigateApp:
        for detector in self.detectors.values():
            detector.stop()

-        empty_and_close_queue(self.detection_queue)
-        logger.info("Detection queue closed")
+        for detection_queue in self.detection_queues.values():
+            empty_and_close_queue(detection_queue)
+
+        logger.info("Detection queues closed")

        self.detected_frames_processor.join()
        empty_and_close_queue(self.detected_frames_queue)
@@ -18,6 +18,7 @@ from frigate.config.camera.updater import (
    CameraConfigUpdateEnum,
    CameraConfigUpdateSubscriber,
 )
+from frigate.detectors.detector_config import NON_LOGO_ATTRIBUTES

 logger = logging.getLogger(__name__)

@@ -103,13 +104,12 @@ class CameraActivityManager:
        all_objects: list[dict[str, Any]] = []

        for camera in new_activity.keys():
-            camera_config = self.config.cameras.get(camera)
-            if camera_config is None:
+            if camera not in self.config.cameras:
                continue

            # handle cameras that were added dynamically
            if camera not in self.camera_all_object_counts:
-                self.__init_camera(camera_config)
+                self.__init_camera(self.config.cameras[camera])

            new_objects = new_activity[camera].get("objects", [])
            all_objects.extend(new_objects)
@@ -179,7 +179,7 @@ class CameraActivityManager:
            return

        for label in camera_config.objects.track:
-            if label in self.config.model.non_logo_attributes:
+            if label in NON_LOGO_ATTRIBUTES:
                continue

            new_count = all_objects[label]
@@ -234,13 +234,12 @@ class AudioActivityManager:
        now = datetime.datetime.now().timestamp()

        for camera in new_activity.keys():
-            camera_config = self.config.cameras.get(camera)
-            if camera_config is None:
+            if camera not in self.config.cameras:
                continue

            # handle cameras that were added dynamically
            if camera not in self.current_audio_detections:
-                self.__init_camera(camera_config)
+                self.__init_camera(self.config.cameras[camera])

            new_detections = new_activity[camera].get("detections", [])
            if self.compare_audio_activity(camera, new_detections, now):
@@ -15,7 +15,9 @@ from frigate.config.camera.updater import (
    CameraConfigUpdateSubscriber,
 )
 from frigate.const import REPLAY_CAMERA_PREFIX
+from frigate.detectors.detector_config import SceneEnum
 from frigate.models import Regions
+from frigate.object_detection.util import detection_frame_size
 from frigate.util.builtin import empty_and_close_queue
 from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
 from frigate.util.object import get_camera_regions_grid
@@ -29,7 +31,7 @@ class CameraMaintainer(threading.Thread):
    def __init__(
        self,
        config: FrigateConfig,
-        detection_queue: Queue,
+        detection_queues: dict[SceneEnum, Queue],
        detected_frames_queue: Queue,
        camera_metrics: DictProxy,
        ptz_metrics: dict[str, PTZMetrics],
@@ -38,7 +40,7 @@ class CameraMaintainer(threading.Thread):
    ):
        super().__init__(name="camera_processor")
        self.config = config
-        self.detection_queue = detection_queue
+        self.detection_queues = detection_queues
        self.detected_frames_queue = detected_frames_queue
        self.stop_event = stop_event
        self.camera_metrics = camera_metrics
@@ -79,10 +81,11 @@ class CameraMaintainer(threading.Thread):
        # create or update region grids for each camera
        for camera in self.config.cameras.values():
            assert camera.name is not None
+            model = self.config.model_for_camera(camera.name)
            self.region_grids[camera.name] = get_camera_regions_grid(
                camera.name,
                camera.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
            )

    def __calculate_shm_frame_count(self) -> int:
@@ -114,6 +117,7 @@ class CameraMaintainer(threading.Thread):
            return

        camera_stop_event = self.__ensure_camera_stop_event(name)
+        model = self.config.model_for_camera(name)

        if runtime:
            self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
@@ -123,32 +127,24 @@ class CameraMaintainer(threading.Thread):
            self.region_grids[name] = get_camera_regions_grid(
                name,
                config.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
            )

            try:
-                largest_frame = max(
-                    [
-                        det.model.height * det.model.width * 3
-                        if det.model is not None
-                        else 320
-                        for det in self.config.detectors.values()
-                    ]
-                )
                UntrackedSharedMemory(name=f"out-{name}", create=True, size=20 * 6 * 4)
                UntrackedSharedMemory(
                    name=name,
                    create=True,
-                    size=largest_frame,
+                    size=detection_frame_size(model),
                )
            except FileExistsError:
                pass

        camera_process = CameraTracker(
            config,
-            self.config.model,
-            self.config.model.merged_labelmap,
-            self.detection_queue,
+            model,
+            model.merged_labelmap,
+            self.detection_queues[model.scene],
            self.detected_frames_queue,
            self.camera_metrics[name],
            self.ptz_metrics[name],
@@ -40,6 +40,7 @@ class CameraState:
        self.name = name
        self.config = config
        self.camera_config = config.cameras[name]
+        self.model = config.model_for_camera(name)
        self.frame_manager = frame_manager
        self.best_objects: dict[str, TrackedObject] = {}
        self.tracked_objects: dict[str, TrackedObject] = {}
@@ -60,11 +61,6 @@ class CameraState:
        # face/LPR pipelines when using a model without built-in detection.
        self.face_recognition_min_obj_area: int = 0
        self.lpr_min_obj_area: int = 0
-        self.lp_objects = {
-            label
-            for label, attributes in config.model.attributes_map.items()
-            if "license_plate" in attributes
-        }

        if (
            self.camera_config.face_recognition.enabled
@@ -106,9 +102,7 @@ class CameraState:
                        thickness = 1
                    else:
                        thickness = 2
-                        color = self.config.model.colormap.get(
-                            obj["label"], (255, 255, 255)
-                        )
+                        color = self.model.colormap.get(obj["label"], (255, 255, 255))
                else:
                    thickness = 1
                    color = (255, 0, 0)
@@ -130,9 +124,7 @@ class CameraState:
                    and obj["frame_time"] == frame_time
                ):
                    thickness = 5
-                    color = self.config.model.colormap.get(
-                        obj["label"], (255, 255, 255)
-                    )
+                    color = self.model.colormap.get(obj["label"], (255, 255, 255))

                    # debug autotracking zooming - show the zoom factor box
                    if (
@@ -266,9 +258,7 @@ class CameraState:
        if draw_options.get("paths"):
            for obj in tracked_objects.values():
                if obj["frame_time"] == frame_time and obj["path_data"]:
-                    color = self.config.model.colormap.get(
-                        obj["label"], (255, 255, 255)
-                    )
+                    color = self.model.colormap.get(obj["label"], (255, 255, 255))

                    path_points = [
                        (
@@ -371,7 +361,7 @@ class CameraState:
        for id in new_ids:
            logger.debug(f"{self.name}: New tracked object ID: {id}")
            new_obj = tracked_objects[id] = TrackedObject(
-                self.config.model,
+                self.model,
                self.camera_config,
                self.config.ui,
                self.frame_cache,
@@ -457,7 +447,7 @@ class CameraState:
                and obj_area >= self.face_recognition_min_obj_area
                and updated_obj.obj_data.get("sub_label") is None
            ) or (
-                obj_label in self.lp_objects
+                obj_label in ("car", "motorcycle")
                and self.lpr_min_obj_area > 0
                and obj_area >= self.lpr_min_obj_area
                and updated_obj.obj_data.get("sub_label") is None
@@ -515,7 +505,7 @@ class CameraState:
                sub_label = None

                if obj.obj_data.get("sub_label"):
-                    if obj.obj_data["sub_label"][0] in self.config.model.all_attributes:
+                    if obj.obj_data["sub_label"][0] in self.model.all_attributes:
                        label = obj.obj_data["sub_label"][0]
                    else:
                        label = f"{object_type}-verified"
@@ -553,7 +543,7 @@ class CameraState:
                    current_best.thumbnail_data is not None
                    and obj.thumbnail_data is not None
                    and is_better_thumbnail(
-                        obj.thumbnail_attributes,
+                        object_type,
                        current_best.thumbnail_data,
                        obj.thumbnail_data,
                        self.camera_config.frame_shape,
@@ -1,7 +1,6 @@
 """Facilitates communication between processes."""

 import multiprocessing as mp
-import threading
 from _pickle import UnpicklingError
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
@@ -19,13 +18,11 @@ class ConfigPublisher:
        self.socket = self.context.socket(zmq.PUB)
        self.socket.bind(SOCKET_PUB_SUB)
        self.stop_event: MpEvent = mp.Event()
-        self.lock = threading.Lock()

    def publish(self, topic: str, payload: Any) -> None:
        """There is no communication back to the processes."""
-        with self.lock:
-            self.socket.send_string(topic, flags=zmq.SNDMORE)
-            self.socket.send_pyobj(payload)
+        self.socket.send_string(topic, flags=zmq.SNDMORE)
+        self.socket.send_pyobj(payload)

    def stop(self) -> None:
        self.stop_event.set()
@@ -11,7 +11,7 @@ from frigate.camera.activity_manager import AudioActivityManager, CameraActivity
 from frigate.comms.base_communicator import Communicator
 from frigate.comms.runtime_state import RuntimeStatePersistence
 from frigate.comms.webpush import WebPushClient
-from frigate.config import BirdseyeModeEnum, FrigateConfig
+from frigate.config import BirdseyeModeConfig, FrigateConfig
 from frigate.config.camera.updater import (
    CameraConfigUpdateEnum,
    CameraConfigUpdatePublisher,
@@ -156,10 +156,11 @@ class Dispatcher:
            if camera not in self.config.cameras:
                return None

+            model = self.config.model_for_camera(camera)
            grid = get_camera_regions_grid(
                camera,
                self.config.cameras[camera].detect,
-                max(self.config.model.width, self.config.model.height),
+                max(model.width, model.height),
            )
            return grid

@@ -882,8 +883,9 @@ class Dispatcher:
    def _on_birdseye_mode_command(self, camera_name: str, payload: str) -> None:
        """Callback for birdseye mode topic."""

-        if payload not in ["CONTINUOUS", "MOTION", "OBJECTS"]:
-            logger.info(f"Invalid birdseye_mode command: {payload}")
+        mode = BirdseyeModeConfig.from_mqtt_payload(payload)
+        if mode is None:
+            logger.info("Invalid birdseye_mode command: %s", payload)
            return

        birdseye_settings = self.config.cameras[camera_name].birdseye
@@ -892,7 +894,7 @@ class Dispatcher:
            logger.info(f"Birdseye mode not enabled for {camera_name}")
            return

-        birdseye_settings.mode = BirdseyeModeEnum(payload.lower())
+        birdseye_settings.mode = mode
        logger.info(
            f"Setting birdseye mode for {camera_name} to {birdseye_settings.mode}"
        )
@@ -901,7 +903,9 @@ class Dispatcher:
            CameraConfigUpdateTopic(CameraConfigUpdateEnum.birdseye, camera_name),
            birdseye_settings,
        )
-        self.publish(f"{camera_name}/birdseye_mode/state", payload, retain=True)
+        self.publish(
+            f"{camera_name}/birdseye_mode/state", mode.to_mqtt_payload(), retain=True
+        )

    def _on_camera_notification_command(self, camera_name: str, payload: str) -> None:
        """Callback for camera level notifications topic."""
@@ -1,7 +1,6 @@
 """Facilitates communication between processes."""

 import logging
-import threading
 from collections.abc import Callable
 from enum import Enum
 from typing import Any
@@ -79,21 +78,14 @@ class EmbeddingsRequestor:
        self.context = zmq.Context()
        self.socket = self.context.socket(zmq.REQ)
        self.socket.connect(SOCKET_REP_REQ)
-        self.lock = threading.Lock()

    def send_data(self, topic: str, data: Any) -> Any:
        """Sends data and then waits for reply."""
-        # an overlapping call fails fast so a slow reply can't stall the API
-        if not self.lock.acquire(blocking=False):
-            return ""
-
        try:
            self.socket.send_json((topic, data))
            return self.socket.recv_json()
        except zmq.ZMQError:
            return ""
-        finally:
-            self.lock.release()

    def stop(self) -> None:
        self.socket.close()
@@ -73,16 +73,14 @@ class InterProcessRequestor:
        self.context = zmq.Context()
        self.socket = self.context.socket(zmq.REQ)
        self.socket.connect(SOCKET_REP_REQ)
-        self.lock = threading.Lock()

    def send_data(self, topic: str, data: Any) -> Any:
        """Sends data and then waits for reply."""
-        with self.lock:
-            try:
-                self.socket.send_json((topic, data))
-                return self.socket.recv_json()
-            except zmq.ZMQError:
-                return ""
+        try:
+            self.socket.send_json((topic, data))
+            return self.socket.recv_json()
+        except zmq.ZMQError:
+            return ""

    def stop(self) -> None:
        self.socket.close(linger=0)
@@ -125,7 +125,7 @@ class MqttClient(Communicator):
            self.publish(
                f"{camera_name}/birdseye_mode/state",
                (
-                    camera.birdseye.mode.value.upper()
+                    camera.birdseye.mode.to_mqtt_payload()
                    if camera.birdseye.enabled
                    else "OFF"
                ),
@@ -41,20 +41,6 @@ class PushNotification:
    ttl: int = 0


-def _build_web_pushers(user: str, subs: list[dict[str, Any]]) -> list[WebPusher]:
-    """Build pushers for a user's stored subscriptions, skipping unusable ones."""
-    pushers: list[WebPusher] = []
-
-    for sub in subs:
-        # WebPusher decodes the stored keys and raises on malformed ones
-        try:
-            pushers.append(WebPusher(sub))
-        except Exception:
-            logger.warning("Skipping invalid notification subscription for %s", user)
-
-    return pushers
-
-
 class WebPushClient(Communicator):
    """Frigate wrapper for webpush client."""

@@ -70,7 +56,6 @@ class WebPushClient(Communicator):
            for c in self.config.cameras.values()
        }
        self.suspension_broadcaster: Callable[[str, Any, bool], None] | None = None
-        self.config_lock = threading.Lock()
        self.last_camera_notification_time: dict[str, float] = {
            c.name: 0  # type: ignore[misc]
            for c in self.config.cameras.values()
@@ -97,16 +82,14 @@ class WebPushClient(Communicator):
            User.select(User.username, User.notification_tokens).dicts().iterator()
        )
        for user in users:
-            self.web_pushers[user["username"]] = _build_web_pushers(
-                user["username"], user["notification_tokens"]
-            )
+            self.web_pushers[user["username"]] = []
+            for sub in user["notification_tokens"]:
+                self.web_pushers[user["username"]].append(WebPusher(sub))

        # notification and auth config updater
        self.global_config_subscriber = ConfigSubscriber("config/")
        self.config_subscriber = CameraConfigUpdateSubscriber(
-            self.config,
-            self.config.cameras,
-            [CameraConfigUpdateEnum.add, CameraConfigUpdateEnum.notifications],
+            self.config, self.config.cameras, [CameraConfigUpdateEnum.notifications]
        )
        self._refresh_user_cameras()

@@ -157,7 +140,10 @@ class WebPushClient(Communicator):
                    User.username == user
                ).execute()

-                self.web_pushers[user] = _build_web_pushers(user, user_subs)
+                self.web_pushers[user] = []
+
+                for sub in user_subs:
+                    self.web_pushers[user].append(WebPusher(sub))

                logger.info(
                    f"Cleaned up {len(expired)} notification subscriptions for {user}"
@@ -206,30 +192,27 @@ class WebPushClient(Communicator):

    def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
        """Wrapper for publishing when client is in valid state."""
-        with self.config_lock:
-            # check for updated global config (notifications, auth)
-            while True:
-                config_topic, config_payload = (
-                    self.global_config_subscriber.check_for_update()
-                )
-                if config_topic is None:
-                    break
-                if config_topic == "config/notifications" and config_payload:
-                    self.config.notifications = config_payload
-                elif config_topic == "config/auth":
-                    if isinstance(config_payload, AuthConfig):
-                        self.config.auth = config_payload
-                    self._refresh_user_cameras()
-
-            updates = self.config_subscriber.check_for_updates()
-
-            if "add" in updates:
-                for camera in updates["add"]:
-                    self.suspended_cameras[camera] = 0
-                    self.last_camera_notification_time[camera] = 0
-
+        # check for updated global config (notifications, auth)
+        while True:
+            config_topic, config_payload = (
+                self.global_config_subscriber.check_for_update()
+            )
+            if config_topic is None:
+                break
+            if config_topic == "config/notifications" and config_payload:
+                self.config.notifications = config_payload
+            elif config_topic == "config/auth":
+                if isinstance(config_payload, AuthConfig):
+                    self.config.auth = config_payload
                self._refresh_user_cameras()

+        updates = self.config_subscriber.check_for_updates()
+
+        if "add" in updates:
+            for camera in updates["add"]:
+                self.suspended_cameras[camera] = 0
+                self.last_camera_notification_time[camera] = 0
+
        if topic == "reviews":
            decoded = json.loads(payload)
            camera = decoded["before"]["camera"]
@@ -434,7 +417,6 @@ class WebPushClient(Communicator):
        # Don't notify if message is an update and important fields don't have an update
        if (
            state == "update"
-            and payload["before"]["severity"] == payload["after"]["severity"]
            and len(payload["before"]["data"]["objects"])
            == len(payload["after"]["data"]["objects"])
            and len(payload["before"]["data"]["zones"])
@@ -1,5 +1,3 @@
-from enum import Enum
-
 from pydantic import BaseModel, Field

 from ..base import FrigateBaseModel
@@ -8,22 +6,78 @@ __all__ = [
    "BirdseyeCameraConfig",
    "BirdseyeConfig",
    "BirdseyeLayoutConfig",
-    "BirdseyeModeEnum",
+    "BirdseyeModeConfig",
 ]

+BIRDSEYE_ACTIVITY_TYPES = (
+    "objects",
+    "motion",
+    "stationary_objects",
+    "continuous",
+)

-class BirdseyeModeEnum(str, Enum):
-    objects = "objects"
-    motion = "motion"
-    continuous = "continuous"
+
+class BirdseyeModeConfig(FrigateBaseModel):
+    continuous: bool = Field(
+        default=False,
+        title="Continuous",
+        description="Always include the camera in Birdseye.",
+    )
+    motion: bool = Field(
+        default=False,
+        title="Motion",
+        description="Include the camera in Birdseye when motion is detected.",
+    )
+    objects: bool = Field(
+        default=False,
+        title="Active objects",
+        description="Include the camera in Birdseye while an active object is tracked.",
+    )
+    stationary_objects: bool = Field(
+        default=False,
+        title="Stationary objects",
+        description="Include the camera in Birdseye while a stationary object is tracked.",
+    )

    @classmethod
-    def get_index(cls, type):
-        return list(cls).index(type)
+    def from_mqtt_payload(cls, payload: str) -> "BirdseyeModeConfig | None":
+        """Create mode options from an uppercase MQTT payload."""
+        raw_modes = payload.split(",")
+        if not raw_modes or any(not mode for mode in raw_modes):
+            return None

-    @classmethod
-    def get(cls, index):
-        return list(cls)[index]
+        modes = [mode.lower() for mode in raw_modes]
+        if any(
+            raw_mode != mode.upper() or mode not in BIRDSEYE_ACTIVITY_TYPES
+            for raw_mode, mode in zip(raw_modes, modes)
+        ):
+            return None
+
+        if len(modes) != len(set(modes)):
+            return None
+
+        return cls(**{mode: True for mode in modes})
+
+    def has_enabled_activity(self) -> bool:
+        """Return whether at least one activity type is enabled."""
+        return any(getattr(self, activity) for activity in BIRDSEYE_ACTIVITY_TYPES)
+
+    def to_mqtt_payload(self) -> str:
+        """Serialize enabled mode options for MQTT state topics."""
+        payload = ",".join(
+            activity.upper()
+            for activity in BIRDSEYE_ACTIVITY_TYPES
+            if getattr(self, activity)
+        )
+        if not payload:
+            raise ValueError("At least one Birdseye activity type must be enabled")
+
+        return payload
+
+
+def default_birdseye_mode() -> BirdseyeModeConfig:
+    """Return the default Birdseye mode configuration."""
+    return BirdseyeModeConfig(objects=True)


 class BirdseyeLayoutConfig(FrigateBaseModel):
@@ -47,10 +101,10 @@ class BirdseyeConfig(FrigateBaseModel):
        title="Enable Birdseye",
        description="Enable or disable the Birdseye view feature.",
    )
-    mode: BirdseyeModeEnum = Field(
-        default=BirdseyeModeEnum.objects,
-        title="Tracking mode",
-        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
+    mode: BirdseyeModeConfig = Field(
+        default_factory=default_birdseye_mode,
+        title="Activity types",
+        description="Activity types that include cameras in Birdseye.",
    )

    restream: bool = Field(
@@ -102,10 +156,10 @@ class BirdseyeCameraConfig(BaseModel):
        title="Enable Birdseye",
        description="Enable or disable the Birdseye view feature.",
    )
-    mode: BirdseyeModeEnum = Field(
-        default=BirdseyeModeEnum.objects,
-        title="Tracking mode",
-        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
+    mode: BirdseyeModeConfig = Field(
+        default_factory=default_birdseye_mode,
+        title="Activity types",
+        description="Activity types that include cameras in Birdseye.",
    )

    order: int = Field(
@@ -1,5 +1,7 @@
 from pydantic import Field, model_validator

+from frigate.detectors.detector_config import SceneEnum
+
 from ..base import FrigateBaseModel

 __all__ = ["DetectConfig", "StationaryConfig", "StationaryMaxFramesConfig"]
@@ -60,6 +62,11 @@ class DetectConfig(FrigateBaseModel):
        title="Detect width",
        description="Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
    )
+    scene: SceneEnum | None = Field(
+        default=None,
+        title="Detect scene",
+        description="The environment this camera looks at, used to pick which of the configured models runs on it. Defaults to the model with a scene of 'all'.",
+    )
    fps: int = Field(
        default=5,
        title="Detect FPS",
@@ -11,7 +11,6 @@ from pydantic import (
    BaseModel,
    ConfigDict,
    Field,
-    TypeAdapter,
    ValidationInfo,
    field_validator,
    model_validator,
@@ -19,8 +18,9 @@ from pydantic import (
 from ruamel.yaml import YAML

 from frigate.const import REGEX_JSON
-from frigate.detectors import DetectorConfig, ModelConfig
-from frigate.detectors.detector_config import BaseDetectorConfig
+from frigate.detectors import ModelConfig
+from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.device import DeviceParseError, DeviceSpec, parse_device
 from frigate.plus import PlusApi
 from frigate.util.builtin import (
    deep_merge,
@@ -41,7 +41,7 @@ from .auth import AuthConfig
 from .base import FrigateBaseModel
 from .camera import CameraConfig, CameraLiveConfig
 from .camera.audio import AudioConfig, AudioFilterConfig
-from .camera.birdseye import BirdseyeConfig
+from .camera.birdseye import BirdseyeConfig, BirdseyeModeConfig
 from .camera.detect import DetectConfig
 from .camera.ffmpeg import FfmpegConfig
 from .camera.genai import GenAIConfig, GenAIRoleEnum
@@ -79,9 +79,14 @@ logger = logging.getLogger(__name__)

 yaml = YAML()

-# Pydantic field default applied when an existing config omits `detectors:`.
+# Pydantic field default applied when an existing config omits `models:`.
 # Kept as cpu tflite for backwards compatibility with 0.17 configs.
-DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
+DEFAULT_MODELS = [{"devices": ["cpu"]}]
+
+
+def _default_models() -> list[ModelConfig]:
+    return [ModelConfig.model_validate(model) for model in DEFAULT_MODELS]
+

 # Used by the openvino branch below and rendered into the new-config YAML
 # template so first-time setups default to openvino on CPU.
@@ -93,7 +98,7 @@ DEFAULT_MODEL = {
    "path": "/openvino-model/ssdlite_mobilenet_v2.xml",
    "labelmap_path": "/openvino-model/coco_91cl_bkgr.txt",
 }
-NEW_CONFIG_DETECTORS = {"ov": {"type": "openvino", "device": "CPU"}}
+NEW_CONFIG_MODELS = [{"devices": ["openvino:CPU"], **DEFAULT_MODEL}]
 DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}


@@ -109,7 +114,7 @@ DEFAULT_CONFIG = f"""
 mqtt:
  enabled: False

-{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "model": DEFAULT_MODEL})}
+{_render_default_yaml({"models": NEW_CONFIG_MODELS})}
 cameras: {{}}  # No cameras defined, UI wizard should be used
 version: {CURRENT_CONFIG_VERSION}
 """
@@ -326,8 +331,20 @@ def verify_required_zones_exist(camera_config: CameraConfig) -> None:


 def verify_profile_overrides_match_base(camera_config: CameraConfig) -> None:
-    """Verify that profile zone and mask IDs reference entries defined on the base camera."""
+    """Verify profile overrides against the resolved base camera configuration."""
    for profile_name, profile in camera_config.profiles.items():
+        if profile.birdseye is not None:
+            overrides = profile.birdseye.mode.model_dump(exclude_unset=True)
+            base_mode = camera_config.birdseye.mode.model_dump()
+            resolved_mode = BirdseyeModeConfig.model_validate(
+                deep_merge(overrides, base_mode)
+            )
+            if not resolved_mode.has_enabled_activity():
+                raise ValueError(
+                    f"Camera '{camera_config.name}' profile '{profile_name}' must "
+                    "enable at least one Birdseye activity type"
+                )
+
        if profile.zones:
            for zone_name in profile.zones:
                if zone_name not in camera_config.zones:
@@ -497,16 +514,11 @@ class FrigateConfig(FrigateBaseModel):
        description="User interface preferences such as timezone, time/date formatting, and units.",
    )

-    # Detector config
-    detectors: dict[str, BaseDetectorConfig] = Field(
-        default=DEFAULT_DETECTORS,
-        title="Detector hardware",
-        description="Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
-    )
-    model: ModelConfig = Field(
-        default_factory=ModelConfig,
-        title="Detection model",
-        description="Settings to configure a custom object detection model and its input shape.",
+    # Detection model config
+    models: list[ModelConfig] = Field(
+        default_factory=_default_models,
+        title="Detection models",
+        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene.",
    )

    # GenAI config (named provider configs: name -> GenAIConfig)
@@ -621,11 +633,202 @@ class FrigateConfig(FrigateBaseModel):
    )

    _plus_api: PlusApi
+    _model_devices: dict[SceneEnum, list[DeviceSpec]]
+    _camera_models: dict[str, ModelConfig]
+    _all_attributes: list[str]
+    _all_attribute_logos: list[str]
+    _all_attributes_map: dict[str, list[str]]
+    _all_labels: set[str]

    @property
    def plus_api(self) -> PlusApi:
        return self._plus_api

+    @property
+    def all_attributes(self) -> list[str]:
+        """Every attribute label across all configured models."""
+        return self._all_attributes
+
+    @property
+    def all_attribute_logos(self) -> list[str]:
+        """Every logo attribute label across all configured models."""
+        return self._all_attribute_logos
+
+    @property
+    def all_attributes_map(self) -> dict[str, list[str]]:
+        """Object label to attribute labels, merged across all configured models."""
+        return self._all_attributes_map
+
+    @property
+    def all_labels(self) -> set[str]:
+        """Every object label across all configured models."""
+        return self._all_labels
+
+    @property
+    def primary_model(self) -> ModelConfig:
+        """The model used when no specific camera is in play."""
+        for model in self.models:
+            if model.scene == SceneEnum.all:
+                return model
+
+        return self.models[0]
+
+    def model_for_camera(self, camera_name: str) -> ModelConfig:
+        """Get the detection model a camera runs on.
+
+        Args:
+            camera_name: Name of the camera
+
+        Returns:
+            The model matching the camera's detect scene
+        """
+        return self._camera_models[camera_name]
+
+    def devices_for_model(self, model: ModelConfig) -> list[DeviceSpec]:
+        """Get the parsed hardware devices a model runs on.
+
+        Args:
+            model: One of the configured models
+
+        Returns:
+            The parsed device specs, in config order
+        """
+        return self._model_devices[model.scene]
+
+    def _load_model(self, model: ModelConfig, detector: str) -> ModelConfig:
+        """Apply detector specific defaults to a model and load its weights and labels.
+
+        Args:
+            model: The configured model
+            detector: The detector type the model runs on
+
+        Returns:
+            The loaded model
+        """
+        model_config = model.model_dump(exclude_unset=True, warnings="none")
+
+        if "path" not in model_config:
+            if detector == "cpu" or detector.endswith("_tfl"):
+                model_config["path"] = "/cpu_model.tflite"
+            elif detector == "edgetpu":
+                model_config["path"] = "/edgetpu_model.tflite"
+            elif detector == "openvino":
+                for default_key, default_value in DEFAULT_MODEL.items():
+                    model_config.setdefault(default_key, default_value)
+
+        loaded = ModelConfig.model_validate(model_config)
+        loaded.check_and_load_plus_model(self.plus_api, detector)
+        loaded.compute_model_hash()
+        return loaded
+
+    def _load_models(self) -> None:
+        """Validate the configured models and load each one."""
+        if not self.models:
+            raise ValueError("At least one model must be configured under models")
+
+        model_devices: dict[SceneEnum, list[DeviceSpec]] = {}
+        # device string -> the scene of the model that already claimed it
+        claimed_devices: dict[str, SceneEnum] = {}
+
+        for index, model in enumerate(self.models):
+            scene = model.scene.value
+
+            if model.scene in model_devices:
+                raise ValueError(
+                    f"Multiple models are configured with a scene of '{scene}'. Each model must use a different scene."
+                )
+
+            if not model.devices:
+                raise ValueError(
+                    f"Model '{scene}' must list at least one entry under devices."
+                )
+
+            try:
+                devices = [parse_device(device) for device in model.devices]
+            except DeviceParseError as err:
+                raise ValueError(
+                    f"Model '{scene}' has an invalid device: {err}"
+                ) from err
+
+            detectors = {device.detector for device in devices}
+
+            if len(detectors) > 1:
+                raise ValueError(
+                    f"Model '{scene}' mixes the {', '.join(sorted(detectors))} detectors. All of a model's devices must use the same detector."
+                )
+
+            for device in devices:
+                if device.raw in claimed_devices and not device.shareable:
+                    other = claimed_devices[device.raw]
+                    where = (
+                        f"twice by model '{scene}'"
+                        if other == model.scene
+                        else f"by both the '{other.value}' and '{scene}' models"
+                    )
+                    raise ValueError(
+                        f"Device '{device.raw}' is used {where}, but it can only run one detection process."
+                    )
+
+                claimed_devices[device.raw] = model.scene
+
+            self.models[index] = self._load_model(model, devices[0].detector)
+            model_devices[model.scene] = devices
+
+        attributes: set[str] = set()
+        attribute_logos: set[str] = set()
+        attributes_map: dict[str, set[str]] = {}
+        labels: set[str] = set()
+
+        for model in self.models:
+            attributes.update(model.all_attributes)
+            attribute_logos.update(model.all_attribute_logos)
+            labels.update(model.merged_labelmap.values())
+
+            for label, label_attributes in model.attributes_map.items():
+                attributes_map.setdefault(label, set()).update(label_attributes)
+
+        self._model_devices = model_devices
+        self._all_attributes = sorted(attributes)
+        self._all_attribute_logos = sorted(attribute_logos)
+        self._all_attributes_map = {
+            label: sorted(label_attributes)
+            for label, label_attributes in sorted(attributes_map.items())
+        }
+        self._all_labels = labels
+
+    def _resolve_camera_model(self, name: str, scene: SceneEnum | None) -> ModelConfig:
+        """Resolve which model a camera runs on.
+
+        Args:
+            name: Name of the camera
+            scene: The camera's configured detect scene, if any
+
+        Returns:
+            The model the camera runs on
+        """
+        by_scene = {model.scene: model for model in self.models}
+
+        if scene is not None:
+            model = by_scene.get(scene)
+
+            if model is None:
+                raise ValueError(
+                    f"Camera '{name}' has a detect scene of '{scene.value}', but no model is configured for that scene."
+                )
+
+            return model
+
+        default = by_scene.get(SceneEnum.all) or (
+            self.models[0] if len(self.models) == 1 else None
+        )
+
+        if default is None:
+            raise ValueError(
+                f"Camera '{name}' must set detect -> scene, because more than one model is configured and none of them uses a scene of 'all'."
+            )
+
+        return default
+
    @model_validator(mode="after")
    def post_validation(self, info: ValidationInfo) -> Self:
        # Load plus api from context, if possible.
@@ -670,8 +873,10 @@ class FrigateConfig(FrigateBaseModel):
                    "'embeddings' in its roles for semantic search."
                )

+        self._load_models()
+
        # set default min_score for object attributes
-        for attribute in self.model.all_attributes:
+        for attribute in self.all_attributes:
            existing = self.objects.filters.get(attribute)
            if existing is None:
                self.objects.filters[attribute] = FilterConfig(min_score=0.7)
@@ -721,44 +926,7 @@ class FrigateConfig(FrigateBaseModel):
            exclude_unset=True,
        )

-        for key, detector in self.detectors.items():
-            adapter = TypeAdapter(DetectorConfig)
-            model_dict = (
-                detector
-                if isinstance(detector, dict)
-                else detector.model_dump(warnings="none")
-            )
-            detector_config: BaseDetectorConfig = adapter.validate_python(model_dict)
-
-            # users should not set model themselves
-            if detector_config.model:
-                logger.warning(
-                    "The model key should be specified at the root level of the config, not under detectors. The nested model key will be ignored."
-                )
-                detector_config.model = None
-
-            model_config = self.model.model_dump(exclude_unset=True, warnings="none")
-
-            if detector_config.model_path:
-                model_config["path"] = detector_config.model_path
-
-            if "path" not in model_config:
-                if detector_config.type == "cpu" or detector_config.type.endswith(
-                    "_tfl"
-                ):
-                    model_config["path"] = "/cpu_model.tflite"
-                elif detector_config.type == "edgetpu":
-                    model_config["path"] = "/edgetpu_model.tflite"
-                elif detector_config.type == "openvino":
-                    for default_key, default_value in DEFAULT_MODEL.items():
-                        model_config.setdefault(default_key, default_value)
-
-            model = ModelConfig.model_validate(model_config)
-            model.check_and_load_plus_model(self.plus_api, detector_config.type)
-            model.compute_model_hash()
-            labelmap_objects = model.merged_labelmap.values()
-            detector_config.model = model
-            self.detectors[key] = detector_config
+        self._camera_models = {}

        for name, camera in self.cameras.items():
            modified_global_config = global_config.copy()
@@ -785,6 +953,9 @@ class FrigateConfig(FrigateBaseModel):
                {"name": name, **merged_config}
            )

+            camera_model = self._resolve_camera_model(name, camera_config.detect.scene)
+            self._camera_models[name] = camera_model
+
            if camera_config.ffmpeg.hwaccel_args == "auto":
                camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args

@@ -998,6 +1169,10 @@ class FrigateConfig(FrigateBaseModel):
            self.cameras[name] = camera_config

            verify_config_roles(camera_config)
+            if not camera_config.birdseye.mode.has_enabled_activity():
+                raise ValueError(
+                    f"Camera '{name}' must enable at least one Birdseye activity type"
+                )
            verify_valid_live_stream_names(self, camera_config)
            verify_recording_segments_setup_with_reasonable_time(camera_config)
            verify_zone_objects_are_tracked(camera_config)
@@ -1005,7 +1180,7 @@ class FrigateConfig(FrigateBaseModel):
            verify_profile_overrides_match_base(camera_config)
            verify_autotrack_zones(camera_config)
            verify_motion_and_detect(camera_config)
-            verify_objects_track(camera_config, labelmap_objects)
+            verify_objects_track(camera_config, camera_model.merged_labelmap.values())
            verify_lpr_and_face(self, camera_config)

        # Validate camera profiles reference top-level profile definitions
@@ -1022,8 +1197,16 @@ class FrigateConfig(FrigateBaseModel):
            config.name = name

        self.objects.parse_all_objects(self.cameras)
-        self.model.create_colormap(sorted(self.objects.all_objects))
-        self.model.check_and_load_plus_model(self.plus_api)
+
+        # every model shares one colormap so a label is drawn the same color no
+        # matter which model detected it, so filter attributes across all models
+        # rather than letting each model filter with only its own
+        colored_labels = sorted(
+            set(self.objects.all_objects) - set(self.all_attributes)
+        )
+
+        for model in self.models:
+            model.create_colormap(colored_labels)

        # Check audio transcription and audio detection requirements
        if self.audio_transcription.enabled:
@@ -1,4 +1,3 @@
-import logging
 import os
 import re
 from pathlib import Path
@@ -6,8 +5,6 @@ from typing import Annotated

 from pydantic import AfterValidator, ValidationInfo

-logger = logging.getLogger(__name__)
-
 FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
 secrets_dir = os.environ.get("CREDENTIALS_DIRECTORY", "/run/secrets")
 # read secret files as env vars too
@@ -19,10 +16,6 @@ if os.path.isdir(secrets_dir) and os.access(secrets_dir, os.R_OK):
            )


-# Security overrides that must come from the container environment, which is
-# controlled by the operator, rather than from the config, which admins can edit
-_PROTECTED_ENV_VARS = frozenset({"GO2RTC_ALLOW_ARBITRARY_EXEC"})
-
 # Matches a FRIGATE_* identifier following an opening brace.
 _FRIGATE_IDENT_RE = re.compile(r"FRIGATE_[A-Za-z0-9_]+")

@@ -102,13 +95,6 @@ EnvString = Annotated[str, AfterValidator(validate_env_string)]
 def validate_env_vars(v: dict[str, str], info: ValidationInfo) -> dict[str, str]:
    if isinstance(info.context, dict) and info.context.get("install", False):
        for k, val in v.items():
-            if k.upper() in _PROTECTED_ENV_VARS:
-                logger.warning(
-                    "Ignoring %s in environment_vars, it must be set in the container environment",
-                    k,
-                )
-                continue
-
            os.environ[k] = val
            if k.startswith("FRIGATE_"):
                FRIGATE_ENV_VARS[k] = val
@@ -43,7 +43,9 @@ SECTION_STATE_TOPICS: dict[str, list[tuple[str, Callable[[Any], Any]]]] = {
        ("birdseye", lambda c: "ON" if c.birdseye.enabled else "OFF"),
        (
            "birdseye_mode",
-            lambda c: c.birdseye.mode.value.upper() if c.birdseye.enabled else "OFF",
+            lambda c: (
+                c.birdseye.mode.to_mqtt_payload() if c.birdseye.enabled else "OFF"
+            ),
        ),
    ],
    "detect": [("detect", lambda c: "ON" if c.detect.enabled else "OFF")],
@@ -44,11 +44,7 @@ DEFAULT_ATTRIBUTE_LABEL_MAP = {
        "ups",
        "usps",
    ],
-    "truck": ["license_plate"],
-    "garbage_truck": ["license_plate"],
    "motorcycle": ["license_plate"],
-    "bus": ["license_plate"],
-    "school_bus": ["license_plate"],
 }
 ATTRIBUTE_LABEL_DISPLAY_MAP = {
    "amazon": "Amazon",
@@ -72,7 +72,7 @@ class LicensePlateProcessingMixin:
        # Object config
        self.lp_objects: list[str] = []

-        for obj, attributes in self.config.model.attributes_map.items():
+        for obj, attributes in self.config.all_attributes_map.items():
            if "license_plate" in attributes:
                self.lp_objects.append(obj)

@@ -1290,7 +1290,7 @@ class LicensePlateProcessingMixin:
                and obj_data.get("label") != "license_plate"
            ):
                logger.debug(
-                    f"{camera}: Not a processing license plate for {obj_data.get('label', 'unknown')}."
+                    f"{camera}: Not a processing license plate for non car/motorcycle object."
                )
                return

@@ -1367,7 +1367,7 @@ class LicensePlateProcessingMixin:

                if not license_plate:
                    logger.debug(
-                        f"{camera}: Detected no license plates for {obj_data.get('label', 'unknown')} object."
+                        f"{camera}: Detected no license plates for car/motorcycle object."
                    )
                    return

@@ -105,10 +105,6 @@ class ObjectDescriptionProcessor(PostProcessorApi):
                        logger.debug(f"{camera} sending early request to GenAI")

                        self.early_request_sent[data["id"]] = True
-
-                        if camera_config.objects.genai.debug_save_thumbnails:
-                            self._save_debug_thumbnails(data["id"])
-
                        # Copy thumbnails to avoid holding references after cleanup
                        thumbnails_copy = [
                            data["thumbnail"][:] if data.get("thumbnail") else None
@@ -301,7 +297,26 @@ class ObjectDescriptionProcessor(PostProcessorApi):
        )

        if camera_config.objects.genai.debug_save_thumbnails and num_thumbnails > 0:
-            self._save_debug_thumbnails(event_id)
+            logger.debug(f"Saving {num_thumbnails} thumbnails for event {event_id}")
+
+            Path(os.path.join(CLIPS_DIR, f"genai-requests/{event_id}")).mkdir(
+                parents=True, exist_ok=True
+            )
+
+            for idx, data in enumerate(self.tracked_events[event_id], 1):
+                jpg_bytes: bytes | None = data["thumbnail"]
+
+                if jpg_bytes is None:
+                    logger.warning(f"Unable to save thumbnail {idx} for {event_id}.")
+                else:
+                    with open(
+                        os.path.join(
+                            CLIPS_DIR,
+                            f"genai-requests/{event_id}/{idx}.jpg",
+                        ),
+                        "wb",
+                    ) as j:
+                        j.write(jpg_bytes)

        # Generate the description. Call happens in a thread since it is network bound.
        threading.Thread(
@@ -317,30 +332,6 @@ class ObjectDescriptionProcessor(PostProcessorApi):
        # Clean up tracked events and early request state
        self.cleanup_event(event_id)

-    def _save_debug_thumbnails(self, event_id: str) -> None:
-        """Write the tracked thumbnails for an event to the genai-requests dir."""
-        tracked = self.tracked_events.get(event_id, [])
-        logger.debug(f"Saving {len(tracked)} thumbnails for event {event_id}")
-
-        Path(os.path.join(CLIPS_DIR, f"genai-requests/{event_id}")).mkdir(
-            parents=True, exist_ok=True
-        )
-
-        for idx, data in enumerate(tracked, 1):
-            jpg_bytes: bytes | None = data["thumbnail"]
-
-            if jpg_bytes is None:
-                logger.warning(f"Unable to save thumbnail {idx} for {event_id}.")
-            else:
-                with open(
-                    os.path.join(
-                        CLIPS_DIR,
-                        f"genai-requests/{event_id}/{idx}.jpg",
-                    ),
-                    "wb",
-                ) as j:
-                    j.write(jpg_bytes)
-
    def _genai_embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
        """Embed the description for an event."""
        start = datetime.datetime.now().timestamp()
@@ -231,8 +231,8 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                    final_data,
                    thumbs,
                    camera_config.review.genai,
-                    list(self.config.model.merged_labelmap.values()),
-                    self.config.model.all_attributes,
+                    sorted(self.config.all_labels),
+                    self.config.all_attributes,
                ),
            ).start()

@@ -170,7 +170,12 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
        return None

    def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray) -> None:
-        if not self.model_config.name or not self.model_config.state_config:
+        if (
+            not self.model_config.name
+            or not self.model_config.state_config
+            or not self.tensor_input_details
+            or not self.tensor_output_details
+        ):
            return

        if self.metrics and self.model_config.name in self.metrics.classification_cps:
@@ -510,7 +515,12 @@ class CustomObjectClassificationProcessor(DeferredRealtimeProcessorApi):
        return best_label, avg_score

    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray) -> None:
-        if not self.model_config.name or not self.model_config.object_config:
+        if (
+            not self.model_config.name
+            or not self.model_config.object_config
+            or not self.tensor_input_details
+            or not self.tensor_output_details
+        ):
            return

        if self.metrics and self.model_config.name in self.metrics.classification_cps:
@@ -1,10 +1,8 @@
 import logging
 import sqlite3
-import threading
 from typing import Any

 import regex
-from peewee import DatabaseError
 from playhouse.sqliteq import SqliteQueueDatabase

 logger = logging.getLogger(__name__)
@@ -19,7 +17,6 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
        self.load_vec_extension: bool = load_vec_extension
        # no extension necessary, sqlite will load correctly for each platform
        self.sqlite_vec_path = "/usr/local/lib/vec0"
-        self.upsert_lock = threading.Lock()
        super().__init__(*args, **kwargs)

    def _connect(self, *args: Any, **kwargs: Any) -> sqlite3.Connection:
@@ -56,22 +53,6 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):

        conn.create_function("REGEXP", 2, regexp)

-    def execute_write(self, sql: str, params: Any = None) -> None:
-        """Run a write and wait for it, so that failures are raised here.
-
-        SqliteQueueDatabase hands non-SELECT statements to a writer thread and
-        stores any exception on the cursor it returns, so callers that ignore
-        that cursor never learn the write failed.
-        """
-        self.execute_sql(sql, params).fetchall()
-
-    def _table_exists(self, table: str) -> bool:
-        cursor = self.execute_sql(
-            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
-            (table,),
-        )
-        return cursor.fetchone() is not None
-
    def _delete_embeddings(self, table: str, event_ids: list[str]) -> None:
        """Delete embeddings for the given events, if the table exists.

@@ -82,17 +63,17 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
            return

        # the embeddings tables are only created once semantic search has run
-        if not self._table_exists(table):
+        cursor = self.execute_sql(
+            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
+            (table,),
+        )
+
+        if cursor.fetchone() is None:
            logger.debug("Skipping %s cleanup, table does not exist", table)
            return

        ids = ",".join(["?" for _ in event_ids])
-
-        # callers treat cleanup as best effort, so log rather than propagate
-        try:
-            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
-        except DatabaseError:
-            logger.exception("Failed to delete embeddings from %s", table)
+        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)

    def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
        self._delete_embeddings("vec_thumbnails", event_ids)
@@ -100,67 +81,25 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
    def delete_embeddings_description(self, event_ids: list[str]) -> None:
        self._delete_embeddings("vec_descriptions", event_ids)

-    def _restore_vec_info_table(self, table: str) -> None:
-        """Recreate the _info shadow table a legacy vec0 table is missing.
-
-        sqlite-vec added _info in 0.1.6 and drops it unconditionally when a
-        table is destroyed, so tables written by Frigate 0.17 and earlier fail
-        to drop. An empty stub is enough, and leaving it unseeded keeps the
-        table reading as pre-0.1.10 if the drop does not follow.
-        """
-        if not self._table_exists(table) or self._table_exists(f"{table}_info"):
-            return
-
-        logger.debug("Restoring the %s_info shadow table before dropping", table)
-        self.execute_write(
-            f'CREATE TABLE "{table}_info" (key TEXT PRIMARY KEY, value ANY)'
-        )
-
    def drop_embeddings_tables(self) -> None:
-        for table in ("vec_descriptions", "vec_thumbnails"):
-            self._restore_vec_info_table(table)
-            self.execute_write(f"DROP TABLE IF EXISTS {table}")
+        self.execute_sql("""
+            DROP TABLE vec_descriptions;
+        """)
+        self.execute_sql("""
+            DROP TABLE vec_thumbnails;
+        """)

    def create_embeddings_tables(self) -> None:
        """Create vec0 virtual table for embeddings"""
-        self.execute_write("""
+        self.execute_sql("""
            CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                id TEXT PRIMARY KEY,
                thumbnail_embedding FLOAT[768] distance_metric=cosine
            );
        """)
-        self.execute_write("""
+        self.execute_sql("""
            CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
                id TEXT PRIMARY KEY,
                description_embedding FLOAT[768] distance_metric=cosine
            );
        """)
-
-    def upsert_embeddings(
-        self, table: str, column: str, embeddings: dict[str, bytes]
-    ) -> None:
-        """Write embeddings for the given event ids, replacing any that exist.
-
-        vec0 implements neither REPLACE nor UPSERT, so rows that are already
-        there have to be deleted first.
-        """
-        if not embeddings:
-            return
-
-        event_ids = list(embeddings.keys())
-        ids = ",".join(["?" for _ in event_ids])
-        params: list[Any] = []
-
-        for event_id in event_ids:
-            params.extend((event_id, embeddings[event_id]))
-
-        values = ", ".join(["(?, ?)"] * len(event_ids))
-
-        # reindexing and live embedding run on separate threads, and each write
-        # is queued separately, so the delete and the insert have to be held
-        # together or an interleaved pair fails on the vec0 primary key
-        with self.upsert_lock:
-            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
-            self.execute_write(
-                f"INSERT INTO {table}(id, {column}) VALUES {values}", params
-            )
@@ -25,31 +25,25 @@ def is_arm64_platform() -> bool:
    return machine in ("aarch64", "arm64", "armv8", "armv7l")


-def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
+def get_ort_session_options(
+    is_complex_model: bool = False,
+) -> ort.SessionOptions | None:
    """Get ONNX Runtime session options with appropriate settings.

    Args:
-        model_type: Model being loaded, used to pin its graph optimization level.
+        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.

    Returns:
-        SessionOptions with a pinned optimization level, or None for default settings.
+        SessionOptions with appropriate optimization level, or None for default settings.
    """
-    # Import here to avoid circular imports
-    from frigate.embeddings.types import EnrichmentModelTypeEnum
+    if is_complex_model:
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = (
+            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+        )
+        return sess_options

-    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
-        # below EXTENDED the CUDA EP returns an identical vector for every image,
-        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
-        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
-    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
-        # aggressive optimizations create or expect nodes that don't exist
-        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
-    else:
-        return None
-
-    sess_options = ort.SessionOptions()
-    sess_options.graph_optimization_level = level
-    return sess_options
+    return None


 # Import OpenVINO only when needed to avoid circular dependencies
@@ -121,6 +115,21 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
    """Run ONNX models using ONNX Runtime."""

+    @staticmethod
+    def is_cpu_complex_model(model_type: str) -> bool:
+        """Check if model needs basic optimization level to avoid graph fusion issues.
+
+        Some models (like Jina-CLIP) have issues with aggressive optimizations like
+        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
+        """
+        # Import here to avoid circular imports
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+        return model_type in [
+            EnrichmentModelTypeEnum.jina_v1.value,
+            EnrichmentModelTypeEnum.jina_v2.value,
+        ]
+
    @staticmethod
    def is_migraphx_complex_model(model_type: str) -> bool:
        # Import here to avoid circular imports
@@ -199,20 +208,15 @@ class CudaGraphRunner(BaseModelRunner):
            EnrichmentModelTypeEnum.yolov9_license_plate.value,
        ]

-    # ORT performs two regular runs before it starts capturing, but on some
-    # driver / cuDNN combinations the arena still has to extend on the run that
-    # captures, and cudaMalloc is not allowed during capture. Running with
-    # capture disabled first keeps those allocations outside of the capture.
-    GRAPH_FREE_WARMUP_RUNS = 2
-
    def __init__(self, session: ort.InferenceSession, cuda_device_id: int):
        self._session = session
        self._cuda_device_id = cuda_device_id
-        self._prepared = False
+        self._captured = False
        self._io_binding: ort.IOBinding | None = None
        self._input_name: str | None = None
        self._output_names: list[str] | None = None
        self._input_ortvalue: ort.OrtValue | None = None
+        self._output_ortvalues: ort.OrtValue | None = None

    def get_input_names(self) -> list[str]:
        """Get input names for the model."""
@@ -222,41 +226,35 @@ class CudaGraphRunner(BaseModelRunner):
        """Get the input width of the model."""
        return self._session.get_inputs()[0].shape[3]

-    def _prepare(self, input_name: str, tensor_input: np.ndarray) -> None:
-        """Bind CUDA buffers and warm the session up with capture disabled."""
-        self._io_binding = self._session.io_binding()
-        self._input_name = input_name
-        self._output_names = [o.name for o in self._session.get_outputs()]
-
-        self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
-            tensor_input, "cuda", self._cuda_device_id
-        )
-        self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
-
-        for name in self._output_names:
-            # Bind outputs to CUDA and allow ORT to allocate appropriately
-            self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
-
-        # gpu_graph_id -1 disables capture and replay for the run
-        warmup_options = ort.RunOptions()
-        warmup_options.add_run_config_entry("gpu_graph_id", "-1")
-
-        for _ in range(self.GRAPH_FREE_WARMUP_RUNS):
-            self._session.run_with_iobinding(self._io_binding, warmup_options)
-
-        self._prepared = True
-
    def run(self, input: dict[str, Any]):
        # Extract the single tensor input (assuming one input)
        input_name = list(input.keys())[0]
-        tensor_input = np.ascontiguousarray(input[input_name])
+        tensor_input = input[input_name]
+        tensor_input = np.ascontiguousarray(tensor_input)

-        if not self._prepared:
-            self._prepare(input_name, tensor_input)
-        else:
-            # Replay using updated input
-            self._input_ortvalue.update_inplace(tensor_input)
+        if not self._captured:
+            # Prepare IOBinding with CUDA buffers and let ORT allocate outputs on device
+            self._io_binding = self._session.io_binding()
+            self._input_name = input_name
+            self._output_names = [o.name for o in self._session.get_outputs()]

+            self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
+                tensor_input, "cuda", self._cuda_device_id
+            )
+            self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
+
+            for name in self._output_names:
+                # Bind outputs to CUDA and allow ORT to allocate appropriately
+                self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
+
+            # First IOBinding run to allocate, execute, and capture CUDA Graph
+            ro = ort.RunOptions()
+            self._session.run_with_iobinding(self._io_binding, ro)
+            self._captured = True
+            return self._io_binding.copy_outputs_to_cpu()
+
+        # Replay using updated input, copy results to CPU
+        self._input_ortvalue.update_inplace(tensor_input)
        ro = ort.RunOptions()
        self._session.run_with_iobinding(self._io_binding, ro)
        return self._io_binding.copy_outputs_to_cpu()
@@ -325,12 +323,6 @@ class OpenVINOModelRunner(BaseModelRunner):
        if device in ["GPU", "AUTO", "NPU"]:
            self.ov_core.set_property(device, {"PERFORMANCE_HINT": "LATENCY"})

-        if device in ["GPU", "AUTO"]:
-            try:
-                self.ov_core.set_property("GPU", {"GPU_QUEUE_THROTTLE": "LOW"})
-            except Exception as e:
-                logger.debug(f"GPU_QUEUE_THROTTLE not supported: {e}")
-
        if device == "NPU" and OpenVINOModelRunner.is_detection_model(model_type):
            try:
                self.ov_core.set_property(device, {"NPU_TURBO": "YES"})
@@ -634,7 +626,9 @@ def get_optimized_runner(
    return ONNXModelRunner(
        ort.InferenceSession(
            model_path,
-            sess_options=get_ort_session_options(model_type),
+            sess_options=get_ort_session_options(
+                ONNXModelRunner.is_cpu_complex_model(model_type)
+            ),
            providers=providers,
            provider_options=options,
        ),
@@ -3,7 +3,7 @@ import json
 import logging
 import os
 from enum import Enum
-from typing import Any
+from typing import Any, ClassVar

 import requests
 from pydantic import BaseModel, ConfigDict, Field
@@ -15,6 +15,9 @@ from frigate.util.builtin import generate_color_palette, load_labels

 logger = logging.getLogger(__name__)

+# attributes that are recognized rather than shown as a logo
+NON_LOGO_ATTRIBUTES = ["face", "license_plate"]
+

 class PixelFormatEnum(str, Enum):
    rgb = "rgb"
@@ -44,7 +47,27 @@ class ModelTypeEnum(str, Enum):
    yologeneric = "yolo-generic"


+class SceneEnum(str, Enum):
+    """The camera environment a detection model is intended for."""
+
+    all = "all"
+    indoor = "indoor"
+    outdoor = "outdoor"
+    indoor_thermal = "indoor_thermal"
+    outdoor_thermal = "outdoor_thermal"
+
+
 class ModelConfig(BaseModel):
+    scene: SceneEnum = Field(
+        default=SceneEnum.all,
+        title="Model scene",
+        description="The camera environment this model is used for. Cameras select a model by setting detect.scene to a matching value, and 'all' is used by any camera that does not set one.",
+    )
+    devices: list[str] = Field(
+        default_factory=list,
+        title="Detection hardware",
+        description="Hardware this model runs on, as '<detector>' or '<detector>:<device>' (for example 'edgetpu:pci:0' or 'openvino:GPU'). Listing the same device more than once runs additional inference processes on it.",
+    )
    path: str | None = Field(
        None,
        title="Custom object detector model path",
@@ -111,7 +134,7 @@ class ModelConfig(BaseModel):

    @property
    def non_logo_attributes(self) -> list[str]:
-        return ["face", "license_plate"]
+        return NON_LOGO_ATTRIBUTES

    @property
    def all_attributes(self) -> list[str]:
@@ -201,9 +224,7 @@ class ModelConfig(BaseModel):
            unique_attributes.update(attributes)

        self._all_attributes = list(unique_attributes)
-        self._all_attribute_logos = list(
-            unique_attributes - set(["face", "license_plate"])
-        )
+        self._all_attribute_logos = list(unique_attributes - set(NON_LOGO_ATTRIBUTES))

        self._merged_labelmap = {
            **{int(key): val for key, val in model_info["labelMap"].items()},
@@ -234,6 +255,14 @@ class ModelConfig(BaseModel):


 class BaseDetectorConfig(BaseModel):
+    # how the trailing part of a device string ("openvino:GPU" -> "GPU") maps onto
+    # this detector's fields, and whether the same device may be listed more than
+    # once to run additional inference processes against it. Most accelerators
+    # multiplex fine, so this is opt-out rather than opt-in.
+    device_spec_field: ClassVar[str] = "device"
+    device_spec_type: ClassVar[type] = str
+    shareable: ClassVar[bool] = True
+
    # the type field must be defined in all subclasses
    type: str = Field(
        default="cpu",
@@ -2,7 +2,7 @@ import importlib
 import logging
 import pkgutil
 from enum import Enum
-from typing import Annotated, Union
+from typing import Annotated, Union, get_args

 from pydantic import Field

@@ -39,3 +39,21 @@ DetectorConfig = Annotated[
    Union[tuple(BaseDetectorConfig.__subclasses__())],  # noqa: UP007
    Field(discriminator="type"),
 ]
+
+
+def _discriminator_value(config_class: type[BaseDetectorConfig]) -> str | None:
+    """Read the Literal value of a detector config class' type field."""
+    field = config_class.model_fields.get("type")
+
+    if field is None:
+        return None
+
+    values = get_args(field.annotation)
+    return values[0] if values else None
+
+
+config_types: dict[str, type[BaseDetectorConfig]] = {
+    key: config_class
+    for config_class in BaseDetectorConfig.__subclasses__()
+    if (key := _discriminator_value(config_class)) is not None
+}
@@ -0,0 +1,113 @@
+"""Parsing of detection hardware device strings."""
+
+import logging
+from dataclasses import dataclass
+
+from pydantic import TypeAdapter, ValidationError
+
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelConfig
+from frigate.detectors.detector_types import DetectorConfig, config_types
+
+logger = logging.getLogger(__name__)
+
+_detector_adapter: TypeAdapter[BaseDetectorConfig] = TypeAdapter(DetectorConfig)
+
+
+@dataclass(frozen=True)
+class DeviceSpec:
+    """A parsed `<detector>` or `<detector>:<device>` string."""
+
+    raw: str
+    detector: str
+    device: str | None
+
+    @property
+    def shareable(self) -> bool:
+        """Whether this device may be listed more than once."""
+        return config_types[self.detector].shareable
+
+
+class DeviceParseError(ValueError):
+    pass
+
+
+def parse_device(raw: str) -> DeviceSpec:
+    """Parse a device string into its detector type and detector specific device.
+
+    Args:
+        raw: The configured device string, for example 'edgetpu:pci:0'
+
+    Returns:
+        The parsed spec
+
+    Raises:
+        DeviceParseError: If the detector type is unknown or the device is not
+            valid for that detector
+    """
+    detector, separator, device = raw.partition(":")
+
+    if detector not in config_types:
+        raise DeviceParseError(
+            f"'{raw}' does not name a known detector. Available detectors are {', '.join(sorted(config_types))}"
+        )
+
+    spec = DeviceSpec(raw=raw, detector=detector, device=device if separator else None)
+
+    # surface a bad device now rather than when the detection process starts
+    build_detector_config(spec, None)
+    return spec
+
+
+def build_detector_config(
+    spec: DeviceSpec, model: ModelConfig | None
+) -> BaseDetectorConfig:
+    """Build the detector config a device string describes.
+
+    Args:
+        spec: The parsed device spec
+        model: The model this detector runs, if it has been resolved yet
+
+    Returns:
+        The validated detector config
+
+    Raises:
+        DeviceParseError: If the device is not valid for this detector type
+    """
+    config: dict[str, object] = {"type": spec.detector, "model": model}
+
+    if spec.device is not None:
+        config_class = config_types[spec.detector]
+
+        try:
+            config[config_class.device_spec_field] = config_class.device_spec_type(
+                spec.device
+            )
+        except ValueError as err:
+            raise DeviceParseError(
+                f"'{spec.raw}' is not a valid {spec.detector} device: {err}"
+            ) from err
+
+    try:
+        return _detector_adapter.validate_python(config)
+    except ValidationError as err:
+        raise DeviceParseError(f"'{spec.raw}' is not a valid device: {err}") from err
+
+
+def runner_names(devices: list[DeviceSpec]) -> list[str]:
+    """Build a unique name for each device, since a shareable device may repeat.
+
+    Args:
+        devices: Every device spec across every configured model, in config order
+
+    Returns:
+        A name per device, suffixed with '#2', '#3', etc. on repeats
+    """
+    names: list[str] = []
+    seen: dict[str, int] = {}
+
+    for spec in devices:
+        count = seen.get(spec.raw, 0) + 1
+        seen[spec.raw] = count
+        names.append(spec.raw if count == 1 else f"{spec.raw}#{count}")
+
+    return names
@@ -1,5 +1,5 @@
 import logging
-from typing import Literal
+from typing import ClassVar, Literal

 from pydantic import ConfigDict, Field

@@ -27,6 +27,9 @@ class CpuDetectorConfig(BaseDetectorConfig):
        title="CPU",
    )

+    device_spec_field: ClassVar[str] = "num_threads"
+    device_spec_type: ClassVar[type] = int
+
    type: Literal[DETECTOR_KEY]
    num_threads: int = Field(
        default=3,
@@ -1,7 +1,7 @@
 import logging
 import math
 import os
-from typing import Literal
+from typing import ClassVar, Literal

 import cv2
 import numpy as np
@@ -28,6 +28,9 @@ class EdgeTpuDetectorConfig(BaseDetectorConfig):
        title="EdgeTPU",
    )

+    # a TPU can only be opened by one process
+    shareable: ClassVar[bool] = False
+
    type: Literal[DETECTOR_KEY]
    device: str = Field(
        default=None,
@@ -5,7 +5,7 @@ import shutil
 import urllib.request
 import zipfile
 from queue import Queue
-from typing import Literal
+from typing import ClassVar, Literal

 import cv2
 import numpy as np
@@ -37,6 +37,9 @@ class MemryXDetectorConfig(BaseDetectorConfig):
        title="MemryX",
    )

+    # an accelerator can only be opened by one process
+    shareable: ClassVar[bool] = False
+
    type: Literal[DETECTOR_KEY]
    device: str = Field(
        default="PCIe",
@@ -28,7 +28,7 @@ class OvDetectorConfig(BaseDetectorConfig):

    type: Literal[DETECTOR_KEY]
    device: str = Field(
-        default=None,
+        default="AUTO",
        title="Device Type",
        description="The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU').",
    )
@@ -2,7 +2,7 @@ import logging
 import os.path
 import re
 import urllib.request
-from typing import Literal
+from typing import ClassVar, Literal

 import cv2
 import numpy as np
@@ -35,6 +35,9 @@ class RknnDetectorConfig(BaseDetectorConfig):
        title="RKNN",
    )

+    device_spec_field: ClassVar[str] = "num_cores"
+    device_spec_type: ClassVar[type] = int
+
    type: Literal[DETECTOR_KEY]
    num_cores: int = Field(
        default=0,
@@ -14,7 +14,7 @@ try:
 except ModuleNotFoundError:
    TRT_SUPPORT = False

-from typing import Literal
+from typing import ClassVar, Literal

 from pydantic import ConfigDict, Field

@@ -53,6 +53,8 @@ class TensorRTDetectorConfig(BaseDetectorConfig):
        title="TensorRT",
    )

+    device_spec_type: ClassVar[type] = int
+
    type: Literal[DETECTOR_KEY]
    device: int = Field(
        default=0, title="GPU Device Index", description="The GPU device index to use."
@@ -6,10 +6,9 @@ import logging
 import os
 import threading
 import time
-from typing import Any

 import numpy as np
-from peewee import DatabaseError, DoesNotExist, IntegrityError
+from peewee import DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict

@@ -208,10 +207,12 @@ class Embeddings:
        embedding = self.vision_embedding([thumbnail])[0]

        if upsert:
-            self.db.upsert_embeddings(
-                "vec_thumbnails",
-                "thumbnail_embedding",
-                {event_id: serialize(embedding)},
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
+                VALUES(?, ?)
+                """,
+                (event_id, serialize(embedding)),
            )

        self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -250,12 +251,19 @@ class Embeddings:
        embeddings = self.vision_embedding(valid_thumbs)

        if upsert:
-            items = {}
+            items = []
            for i in range(len(valid_ids)):
-                items[valid_ids[i]] = serialize(embeddings[i])
+                items.append(valid_ids[i])
+                items.append(serialize(embeddings[i]))
                self.image_eps.update()

-            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
+                VALUES {}
+                """.format(", ".join(["(?, ?)"] * len(valid_ids))),
+                items,
+            )

        duration = datetime.datetime.now().timestamp() - start
        self.image_inference_speed.update(duration / len(valid_ids))
@@ -269,10 +277,12 @@ class Embeddings:
        embedding = self.text_embedding([description])[0]

        if upsert:
-            self.db.upsert_embeddings(
-                "vec_descriptions",
-                "description_embedding",
-                {event_id: serialize(embedding)},
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
+                VALUES(?, ?)
+                """,
+                (event_id, serialize(embedding)),
            )

        self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -292,14 +302,19 @@ class Embeddings:

        if upsert:
            ids = list(event_descriptions.keys())
-            items = {}
+            items = []

            for i in range(len(ids)):
-                items[ids[i]] = serialize(embeddings[i])
+                items.append(ids[i])
+                items.append(serialize(embeddings[i]))
                self.text_eps.update()

-            self.db.upsert_embeddings(
-                "vec_descriptions", "description_embedding", items
+            self.db.execute_sql(
+                """
+                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
+                VALUES {}
+                """.format(", ".join(["(?, ?)"] * len(ids))),
+                items,
            )

        self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -307,17 +322,6 @@ class Embeddings:
        return embeddings

    def reindex(self) -> None:
-        """Rebuild every tracked object embedding from scratch."""
-        totals: dict[str, Any] = {"status": "indexing"}
-
-        try:
-            self._reindex(totals)
-        except DatabaseError:
-            logger.exception("Unable to reindex tracked object embeddings")
-            totals["status"] = "failed"
-            self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
-
-    def _reindex(self, totals: dict[str, Any]) -> None:
        logger.info("Indexing tracked object embeddings...")

        self.db.drop_embeddings_tables()
@@ -342,18 +346,14 @@ class Embeddings:
            batch_size = 32
        current_page = 1

-        totals.update(
-            {
-                "thumbnails": 0,
-                "descriptions": 0,
-                "processed_objects": total_events - 1
-                if total_events < batch_size
-                else 0,
-                "total_objects": total_events,
-                "time_remaining": 0 if total_events < batch_size else -1,
-                "status": "indexing",
-            }
-        )
+        totals = {
+            "thumbnails": 0,
+            "descriptions": 0,
+            "processed_objects": total_events - 1 if total_events < batch_size else 0,
+            "total_objects": total_events,
+            "time_remaining": 0 if total_events < batch_size else -1,
+            "status": "indexing",
+        }

        self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)

@@ -365,7 +365,6 @@ class EventCleanup(threading.Thread):
                    chunk = ids_to_delete[i : i + CHUNK_SIZE]
                    logger.debug(f"Deleting {len(chunk)} events from the database")
                    Event.delete().where(Event.id << chunk).execute()
-                    Timeline.delete().where(Timeline.source_id << chunk).execute()

                    # embeddings are always cleaned up, even when semantic search
                    # is disabled, so that they don't outlive their events
@@ -159,7 +159,8 @@ class EventProcessor(threading.Thread):
            if width is None or height is None:
                return

-            first_detector = list(self.config.detectors.values())[0]
+            camera_model = self.config.model_for_camera(camera)
+            camera_detector = self.config.devices_for_model(camera_model)[0].detector

            start_time = event_data["start_time"]
            end_time = (
@@ -229,13 +230,9 @@ class EventProcessor(threading.Thread):
                Event.thumbnail: event_data.get("thumbnail"),
                Event.has_clip: event_data["has_clip"],
                Event.has_snapshot: event_data["has_snapshot"],
-                Event.model_hash: first_detector.model.model_hash
-                if first_detector.model
-                else None,
-                Event.model_type: first_detector.model.model_type
-                if first_detector.model
-                else None,
-                Event.detector_type: first_detector.type,
+                Event.model_hash: camera_model.model_hash,
+                Event.model_type: camera_model.model_type,
+                Event.detector_type: camera_detector,
                Event.data: {
                    "box": box,
                    "region": region,
@@ -121,8 +121,8 @@ PRESETS_HW_ACCEL_SCALE = {
    "preset-rpi-64-h264": "-r {0} -vf fps={0},scale={1}:{2}",
    "preset-rpi-64-h265": "-r {0} -vf fps={0},scale={1}:{2}",
    FFMPEG_HWACCEL_VAAPI: "-r {0} -vf fps={0},scale_vaapi=w={1}:h={2},hwdownload,format=nv12",
-    "preset-intel-qsv-h264": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
-    "preset-intel-qsv-h265": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
+    "preset-intel-qsv-h264": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
+    "preset-intel-qsv-h265": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
    FFMPEG_HWACCEL_NVIDIA: "-r {0} -vf fps={0},scale_cuda=w={1}:h={2},hwdownload,format=nv12",
    "preset-jetson-h264": "-r {0}",  # scaled in decoder
    "preset-jetson-h265": "-r {0}",  # scaled in decoder
@@ -245,7 +245,7 @@ class GeminiClient(GenAIClient):
                    )
                    gemini_messages.append(
                        types.Content(
-                            role="user",
+                            role="function",
                            parts=[
                                types.Part.from_function_response(
                                    name=msg.get("name")
@@ -501,7 +501,7 @@ class GeminiClient(GenAIClient):
                    )
                    gemini_messages.append(
                        types.Content(
-                            role="user",
+                            role="function",
                            parts=[
                                types.Part.from_function_response(
                                    name=msg.get("name")
@@ -262,6 +262,10 @@ def get_tool_definitions(
    `attribute` parameter is exposed for filtering by their labels. When the
    embeddings model only understands English (JinaV1), the `semantic_query`
    description instructs the model to write the query in English.
+
+    Descriptions here stay mechanical: which tool to reach for, and how the
+    filters relate to each other, is stated once in the system prompt so the
+    guidance is not paid for twice on every request.
    """
    search_objects_properties: dict[str, Any] = {
        "camera": {
@@ -270,26 +274,13 @@ def get_tool_definitions(
        },
        "label": {
            "type": "string",
-            "description": (
-                "Generic object class to filter by — one of the tracked detector "
-                "labels such as 'person', 'package', 'car', 'dog', 'bird'. Use "
-                "this for broad queries like 'show me all cars today'. Combine "
-                "with semantic_query when the user also describes appearance or "
-                "behavior (e.g. label='person', semantic_query='riding a lawn "
-                "mower')."
-            ),
+            "description": "Tracked object class to filter by.",
        },
        "sub_label": {
            "type": "string",
            "description": (
-                "Filter by a DISCRETE NAMED entity recognized in the detection. "
-                "Use this for: a known person's name ('John'), a delivery "
-                "company ('Amazon', 'UPS'), a recognized animal species or "
-                "breed ('blue jay', 'cardinal', 'golden retriever'), or a "
-                "license plate string. When filtering by a specific name, set "
-                "only sub_label and leave label unset. Do NOT use sub_label "
-                "for descriptions of appearance, clothing, or actions — those "
-                "belong in semantic_query."
+                "Name recognized in the detection: a person, delivery company, "
+                "animal species or breed, or license plate."
            ),
        },
        "after": {
@@ -313,20 +304,11 @@ def get_tool_definitions(
    }

    if attribute_classifications:
-        model_outline = "; ".join(
-            f"{m['name']} (applies to {', '.join(m['objects']) or 'any object'})"
-            for m in attribute_classifications
-        )
        search_objects_properties["attribute"] = {
            "type": "string",
            "description": (
-                "Filter by a classification attribute label produced by a "
-                "configured attribute classification model. Use this INSTEAD "
-                "of semantic_query when the user's request matches one of "
-                "these classifications. Configured models: "
-                f"{model_outline}. "
-                "Set the value to the attribute label that matches the user's "
-                "phrasing (case-sensitive)."
+                "Attribute label produced by a configured classification model "
+                "(case-sensitive)."
            ),
        }

@@ -334,29 +316,12 @@ def get_tool_definitions(
        search_objects_properties["semantic_query"] = {
            "type": "string",
            "description": (
-                "Optional natural-language description of a PHYSICAL "
-                "CHARACTERISTIC, APPEARANCE, or ACTIVITY the user mentioned, "
-                "used to semantically narrow results. Only set this when the "
-                "user describes something beyond what label and sub_label can "
-                "express on their own.\n"
-                "USE for descriptive phrases like: 'riding a lawn mower', "
-                "'wearing a red jacket', 'carrying a package', 'walking a "
-                "dog', 'on a bicycle', 'holding an umbrella'.\n"
-                "DO NOT USE for:\n"
-                "- specific named people, pets, or delivery companies → use sub_label\n"
-                "- animal species or breed names like 'blue jay', 'cardinal', "
-                "'golden retriever' → use sub_label\n"
-                "- license plate strings → use sub_label\n"
-                "- generic object queries like 'all cars today' or 'every "
-                "person' → use label alone with no semantic_query\n"
-                "When set, combine with label/time/camera/zone filters as "
-                "usual (e.g. label='person', semantic_query='riding a lawn "
-                "mower', after='2024-05-01T00:00:00Z')."
+                "Description of an appearance or activity, used to semantically "
+                "narrow results."
                + (
-                    " The configured embeddings model only understands "
-                    "English, so always write semantic_query in English, "
-                    "translating the user's description if they phrased it "
-                    "in another language."
+                    " The configured embeddings model only understands English, so "
+                    "always write this in English, translating the user's "
+                    "description if they phrased it in another language."
                    if embeddings_language == "english"
                    else ""
                )
@@ -364,26 +329,10 @@ def get_tool_definitions(
        }

    search_objects_description = (
-        "Search the historical record of detected objects in Frigate. "
-        "Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
-        "'when was the last car?', 'show me detections from yesterday'. "
-        "Do NOT use this for monitoring or alerting requests about future events — "
-        "use start_camera_watch instead for those. "
-        "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car).\n\n"
-        "Choose filters based on what the user is asking for:\n"
-        "- Generic class query ('show me all cars today'): set `label` only.\n"
-        "- Specific NAMED entity (known person, delivery company, animal "
-        "species/breed like 'blue jay' or 'golden retriever', license "
-        "plate): set `sub_label` only and leave `label` unset.\n"
+        "Search the historical record of tracked detections. Use this ONLY for "
+        "questions about the PAST, e.g. 'did anyone come by today?', 'when was the "
+        "last car?'. For alerting on future events use start_camera_watch instead."
    )
-    if semantic_search_enabled:
-        search_objects_description += (
-            "- Physical CHARACTERISTIC, APPEARANCE, or ACTIVITY that is not a "
-            "discrete name ('person riding a lawn mower', 'someone in a red "
-            "jacket', 'person carrying a package'): set `semantic_query` with "
-            "the descriptive phrase, optionally alongside `label` for the "
-            "object class. Do NOT put descriptive phrases in sub_label."
-        )

    return [
        {
@@ -398,20 +347,30 @@ def get_tool_definitions(
                "required": [],
            },
        },
+        {
+            "type": "function",
+            "function": {
+                "name": "get_categorized_object_names",
+                "description": (
+                    "Every name that can be attached as a sub_label, grouped by object "
+                    "type: recognized faces, named license plates, classification "
+                    "categories, and delivery logos. Takes no arguments and always "
+                    "returns the complete map."
+                ),
+                "parameters": {
+                    "type": "object",
+                    "properties": {},
+                    "required": [],
+                },
+            },
+        },
        {
            "type": "function",
            "function": {
                "name": "find_similar_objects",
                "description": (
-                    "Find tracked objects that are visually and semantically similar "
-                    "to a specific past event. Use this when the user references a "
-                    "particular object they have seen and wants to find other "
-                    "sightings of the same or similar one ('that green car', 'the "
-                    "person in the red jacket', 'the package that was delivered'). "
-                    "Prefer this over search_objects whenever the user's intent is "
-                    "'find more like this specific one.' Use search_objects first "
-                    "only if you need to locate the anchor event. Requires semantic "
-                    "search to be enabled."
+                    "Find tracked objects visually and semantically similar to a "
+                    "specific past event. Requires semantic search to be enabled."
                ),
                "parameters": {
                    "type": "object",
@@ -473,9 +432,8 @@ def get_tool_definitions(
            "function": {
                "name": "set_camera_state",
                "description": (
-                    "Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
-                    "Use camera='*' to apply to all cameras at once. "
-                    "Only call this tool when the user explicitly asks to change a camera setting. "
+                    "Change a camera's feature state, e.g. turn detection on or off. "
+                    "Only call this when the user explicitly asks to change a setting. "
                    "Requires admin privileges."
                ),
                "parameters": {
@@ -510,14 +468,14 @@ def get_tool_definitions(
                            ],
                            "description": (
                                "The feature to change. Most features accept ON or OFF. "
-                                "birdseye_mode accepts CONTINUOUS, MOTION, or OBJECTS. "
+                                "birdseye_mode accepts CONTINUOUS, MOTION, OBJECTS, STATIONARY_OBJECTS, or a comma-separated combination. "
                                "motion_contour_area and motion_threshold accept a number. "
                                "profile accepts a profile name or 'none' to deactivate (requires camera='*')."
                            ),
                        },
                        "value": {
                            "type": "string",
-                            "description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
+                            "description": "The value to set, as accepted by the chosen feature.",
                        },
                    },
                    "required": ["camera", "feature", "value"],
@@ -529,11 +487,9 @@ def get_tool_definitions(
            "function": {
                "name": "get_live_context",
                "description": (
-                    "Get the current live image and detection information for a single camera: objects being tracked, "
-                    "zones, timestamps. Use this to understand what is visible in the live view. "
-                    "Call this when answering questions about what is happening right now on a specific camera. "
-                    "Operates on one camera at a time; call the tool again for each additional camera. "
-                    "Wildcards and empty values are not accepted."
+                    "Current live image and detections (tracked objects, zones, "
+                    "timestamps) for one camera. Use this for questions about what is "
+                    "happening right now. Call it again for each additional camera."
                ),
                "parameters": {
                    "type": "object",
@@ -541,8 +497,8 @@ def get_tool_definitions(
                        "camera": {
                            "type": "string",
                            "description": (
-                                "Exact name of a single camera to get live context for. "
-                                "Wildcards (e.g. '*', 'all') and empty strings are not accepted."
+                                "Exact name of a single camera. Wildcards (e.g. '*', "
+                                "'all') and empty strings are not accepted."
                            ),
                        },
                    },
@@ -555,10 +511,9 @@ def get_tool_definitions(
            "function": {
                "name": "start_camera_watch",
                "description": (
-                    "Start a continuous VLM watch job that monitors a camera and sends a notification "
-                    "when a specified condition is met. Use this when the user wants to be alerted about "
-                    "a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
-                    "Only one watch job can run at a time. Returns a job ID."
+                    "Start a continuous watch job that monitors a camera and notifies "
+                    "the user when a condition is met, e.g. 'tell me when guests "
+                    "arrive'. Only one watch job can run at a time. Returns a job ID."
                ),
                "parameters": {
                    "type": "object",
@@ -598,10 +553,7 @@ def get_tool_definitions(
            "type": "function",
            "function": {
                "name": "stop_camera_watch",
-                "description": (
-                    "Cancel the currently running VLM watch job. Use this when the user wants to "
-                    "stop a previously started watch, e.g. 'stop watching the front door'."
-                ),
+                "description": "Cancel the currently running watch job.",
                "parameters": {
                    "type": "object",
                    "properties": {},
@@ -614,11 +566,9 @@ def get_tool_definitions(
            "function": {
                "name": "get_profile_status",
                "description": (
-                    "Get the current profile status including the active profile and "
-                    "timestamps of when each profile was last activated. Use this to "
-                    "determine time periods for recap requests — e.g. when the user asks "
-                    "'what happened while I was away?', call this first to find the relevant "
-                    "time window based on profile activation history."
+                    "Get the active profile and when each profile was last activated. "
+                    "Call this before get_recap to derive the time window for requests "
+                    "like 'what happened while I was away?'."
                ),
                "parameters": {
                    "type": "object",
@@ -632,11 +582,9 @@ def get_tool_definitions(
            "function": {
                "name": "get_recap",
                "description": (
-                    "Get a recap of all activity (alerts and detections) for a given time period. "
-                    "Use this after calling get_profile_status to retrieve what happened during "
-                    "a specific window — e.g. 'what happened while I was away?'. Returns a "
-                    "chronological list of activity with camera, objects, zones, and GenAI-generated "
-                    "descriptions when available. Summarize the results for the user."
+                    "Get all activity (alerts and detections) for a time period, as a "
+                    "chronological list with camera, objects, zones, and descriptions "
+                    "when available. Summarize the results for the user."
                ),
                "parameters": {
                    "type": "object",
@@ -723,14 +671,13 @@ def build_chat_system_prompt(
        )
        speed_units_section = f"\n\nReport object speeds to the user in {speed_unit}."

-    semantic_search_section = ""
+    filter_routing_section = (
+        "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
+        "- Generic class ('show me all cars today'): set `label` only.\n"
+        "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset. Call get_categorized_object_names first and use the exact spelling it returns; a guessed spelling matches nothing. If the name is absent, say it is not configured rather than searching for it."
+    )
    if semantic_search_enabled:
-        semantic_search_section = (
-            "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
-            "- Generic class ('show me all cars today'): set `label` only.\n"
-            "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'cardinal', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset.\n"
-            "- Physical characteristic, appearance, or activity that is NOT a discrete name ('find me people riding a lawn mower', 'someone in a red jacket', 'a person carrying a package'): set `semantic_query` with the descriptive phrase, optionally combined with `label` for the object class. Never put descriptive phrases in `sub_label`."
-        )
+        filter_routing_section += "\n- Physical characteristic, appearance, or activity that is NOT a discrete name ('riding a lawn mower', 'someone in a red jacket'): set `semantic_query` with the descriptive phrase, optionally combined with `label`. Never put descriptive phrases in `sub_label`."

    attribute_classification_section = ""
    if attribute_classifications:
@@ -739,9 +686,9 @@ def build_chat_system_prompt(
            for m in attribute_classifications
        )
        attribute_classification_section = (
-            "\n\nAttribute classification models are configured for the following object types:\n"
+            "\n\nConfigured attribute classification models:\n"
            f"{model_lines}\n"
-            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases that fall outside the configured attribute labels."
+            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label (case-sensitive) rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases outside the configured attribute labels."
        )

    return f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
@@ -750,9 +697,6 @@ Current server local date and time: {current_date_str} at {current_time_str}

 Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.

-Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
-When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
-When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
-Always be accurate with time calculations based on the current date provided.
+Always present times in the server's local timezone. When tool results include start_time_local and end_time_local, quote those strings exactly; never convert or invent timestamps, and fall back to UTC or ISO format only when a result has no local time fields. Resolve relative dates like "today" or "this week" against the current date above, and pass dates to tools in ISO 8601 (e.g. {current_date_str}T00:00:00Z for the start of today).

-When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{semantic_search_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
+When the user refers to a specific object they have seen ("that green car", "the person in the red jacket", "a package left today"), prefer find_similar_objects over search_objects, using search_objects only to locate the anchor event and passing its id along. Keep search_objects for generic queries like "show me all cars today". If a user message begins with [attached_event:<id>], treat that id as the anchor for any similarity or "tell me more" request in the same message.{filter_routing_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
@@ -115,10 +115,6 @@ def query_recordings(source_camera: str, start_ts: float, end_ts: float) -> Mode
    return cast(ModelSelect, query)


-class NoRecordingsError(ValueError):
-    """Raised when no recordings exist in the requested time range."""
-
-
 class DebugReplaySource(ABC):
    """Abstract source for a debug replay session.

@@ -191,7 +187,7 @@ class RecordingDebugReplaySource(DebugReplaySource):
            raise ValueError("End time must be after start time")

        if not query_recordings(self._camera, self._start_ts, self._end_ts).count():
-            raise NoRecordingsError(
+            raise ValueError(
                f"No recordings found for camera '{self._camera}' in the specified time range"
            )

@@ -5,7 +5,19 @@ import threading

 from numpy import ndarray

-from frigate.detectors.detector_config import InputTensorEnum
+from frigate.detectors.detector_config import InputTensorEnum, ModelConfig
+
+
+def detection_frame_size(model: ModelConfig) -> int:
+    """Get the shared memory size a camera needs to hand frames to a model.
+
+    Args:
+        model: The model the camera runs on
+
+    Returns:
+        Size in bytes of one model input frame
+    """
+    return model.height * model.width * 3


 class RequestStore:
@@ -9,6 +9,7 @@ import queue
 import subprocess as sp
 import threading
 import traceback
+from dataclasses import dataclass
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any

@@ -16,7 +17,7 @@ import cv2
 import numpy as np

 from frigate.comms.inter_process import InterProcessRequestor
-from frigate.config import BirdseyeModeEnum, FfmpegConfig, FrigateConfig
+from frigate.config import BirdseyeModeConfig, FfmpegConfig, FrigateConfig
 from frigate.const import BASE_DIR, BIRDSEYE_PIPE, INSTALL_DIR, UPDATE_BIRDSEYE_LAYOUT
 from frigate.output.ws_auth import ws_has_camera_access
 from frigate.util.image import (
@@ -28,6 +29,15 @@ from frigate.util.image import (
 logger = logging.getLogger(__name__)


+@dataclass(frozen=True, slots=True)
+class BirdseyeActivity:
+    """Activity signals used to decide whether a camera is shown in Birdseye."""
+
+    has_active_object: bool
+    has_stationary_object: bool
+    has_motion: bool
+
+
 def get_standard_aspect_ratio(width: int, height: int) -> tuple[int, int]:
    """Ensure that only standard aspect ratios are used."""
    # it is important that all ratios have the same scale
@@ -409,18 +419,16 @@ class BirdsEyeFrameManager:
        )

    def camera_active(
-        self, mode: Any, object_box_count: int, motion_box_count: int
+        self,
+        mode: BirdseyeModeConfig,
+        activity: BirdseyeActivity,
    ) -> bool:
-        if mode == BirdseyeModeEnum.continuous:
-            return True
-
-        if mode == BirdseyeModeEnum.motion and motion_box_count > 0:
-            return True
-
-        if mode == BirdseyeModeEnum.objects and object_box_count > 0:
-            return True
-
-        return False
+        return (
+            mode.continuous
+            or (mode.motion and activity.has_motion)
+            or (mode.objects and activity.has_active_object)
+            or (mode.stationary_objects and activity.has_stationary_object)
+        )

    def get_camera_coordinates(self) -> dict[str, dict[str, int]]:
        """Return the coordinates of each camera in the current layout."""
@@ -604,112 +612,92 @@ class BirdsEyeFrameManager:
    ) -> list[list[Any]] | None:
        """Calculate the optimal layout for 2+ cameras."""

-        def map_layout(
-            camera_layout: list[list[Any]], row_height: int
-        ) -> tuple[int, int, list[list[Any]] | None]:
-            """Map the calculated layout."""
-            candidate_layout = []
-            starting_x = 0
-            x = 0
-            max_width = 0
-            y = 0
+        def find_available_x(
+            current_x: int,
+            width: int,
+            reserved_ranges: list[tuple[int, int]],
+            max_width: int,
+        ) -> int | None:
+            """Find the first horizontal slot that does not collide with reservations."""
+            x = current_x

-            for row in camera_layout:
-                final_row = []
-                max_width = max(max_width, x)
-                x = starting_x
-                for cameras in row:
-                    camera_dims = self.cameras[cameras[0]]["dimensions"].copy()
-                    camera_aspect = cameras[1]
+            for reserved_start, reserved_end in sorted(reserved_ranges):
+                if x >= reserved_end:
+                    continue

-                    if camera_dims[1] > camera_dims[0]:
-                        scaled_height = int(row_height * 2)
-                        scaled_width = int(scaled_height * camera_aspect)
-                        starting_x = scaled_width
-                    else:
-                        scaled_height = row_height
-                        scaled_width = int(scaled_height * camera_aspect)
+                if x + width <= reserved_start:
+                    return x

-                    # layout is too large
-                    if (
-                        x + scaled_width > self.canvas.width
-                        or y + scaled_height > self.canvas.height
-                    ):
-                        return x + scaled_width, y + scaled_height, None
+                x = max(x, reserved_end)

-                    final_row.append((cameras[0], (x, y, scaled_width, scaled_height)))
-                    x += scaled_width
+            if x + width <= max_width:
+                return x

-                y += row_height
-                candidate_layout.append(final_row)
-
-            if max_width == 0:
-                max_width = x
-
-            return max_width, y, candidate_layout
-
-        canvas_aspect_x, canvas_aspect_y = self.canvas.get_aspect(coefficient)
-        camera_layout: list[list[Any]] = []
-        camera_layout.append([])
-        starting_x = 0
-        x = starting_x
-        y = 0
-        y_i = 0
-        max_y = 0
-        for camera in cameras_to_add:
-            camera_dims = self.cameras[camera]["dimensions"].copy()
-            camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
-                camera, camera_dims[0], camera_dims[1]
-            )
-
-            if camera_dims[1] > camera_dims[0]:
-                portrait = True
-            else:
-                portrait = False
-
-            if (x + camera_aspect_x) <= canvas_aspect_x:
-                # insert if camera can fit on current row
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-
-                if portrait:
-                    starting_x = camera_aspect_x
-                else:
-                    max_y = max(
-                        max_y,
-                        camera_aspect_y,
-                    )
-
-                x += camera_aspect_x
-            else:
-                # move on to the next row and insert
-                y += max_y
-                y_i += 1
-                camera_layout.append([])
-                x = starting_x
-
-                if x + camera_aspect_x > canvas_aspect_x:
-                    return None
-
-                camera_layout[y_i].append(
-                    (
-                        camera,
-                        camera_aspect_x / camera_aspect_y,
-                    )
-                )
-                x += camera_aspect_x
-
-        if y + max_y > canvas_aspect_y:
            return None

-        row_height = int(self.canvas.height / coefficient)
-        total_width, total_height, standard_candidate_layout = map_layout(
-            camera_layout, row_height
-        )
+        def map_layout(row_height: int) -> tuple[int, int, list[list[Any]] | None]:
+            """Lay out cameras row by row while reserving portrait spans for the next row."""
+            candidate_layout: list[list[Any]] = []
+            reserved_ranges: dict[int, list[tuple[int, int]]] = {}
+            current_row: list[Any] = []
+            row_index = 0
+            row_y = 0
+            row_x = 0
+            max_width = 0
+            max_height = 0
+
+            for camera in cameras_to_add:
+                camera_dims = self.cameras[camera]["dimensions"].copy()
+                camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
+                    camera, camera_dims[0], camera_dims[1]
+                )
+                portrait = camera_dims[1] > camera_dims[0]
+                scaled_height = row_height * 2 if portrait else row_height
+                scaled_width = int(scaled_height * (camera_aspect_x / camera_aspect_y))
+
+                while True:
+                    x = find_available_x(
+                        row_x,
+                        scaled_width,
+                        reserved_ranges.get(row_index, []),
+                        self.canvas.width,
+                    )
+
+                    if x is not None and row_y + scaled_height <= self.canvas.height:
+                        current_row.append(
+                            (camera, (x, row_y, scaled_width, scaled_height))
+                        )
+                        row_x = x + scaled_width
+                        max_width = max(max_width, row_x)
+                        max_height = max(max_height, row_y + scaled_height)
+
+                        if portrait:
+                            reserved_ranges.setdefault(row_index + 1, []).append(
+                                (x, row_x)
+                            )
+
+                        break
+
+                    if current_row:
+                        candidate_layout.append(current_row)
+                        current_row = []
+
+                    row_index += 1
+                    row_y = row_index * row_height
+                    row_x = 0
+
+                    if row_y + scaled_height > self.canvas.height:
+                        overflow_width = max(max_width, scaled_width)
+                        overflow_height = row_y + scaled_height
+                        return overflow_width, overflow_height, None
+
+            if current_row:
+                candidate_layout.append(current_row)
+
+            return max_width, max_height, candidate_layout
+
+        row_height = max(1, int(self.canvas.height / coefficient))
+        total_width, total_height, standard_candidate_layout = map_layout(row_height)

        if not standard_candidate_layout:
            # if standard layout didn't work
@@ -718,9 +706,9 @@ class BirdsEyeFrameManager:
                total_width / self.canvas.width,
                total_height / self.canvas.height,
            )
-            row_height = int(row_height / scale_down_percent)
+            row_height = max(1, int(row_height / scale_down_percent))
            total_width, total_height, standard_candidate_layout = map_layout(
-                camera_layout, row_height
+                row_height
            )

            if not standard_candidate_layout:
@@ -734,8 +722,8 @@ class BirdsEyeFrameManager:
            1 / (total_width / self.canvas.width),
            1 / (total_height / self.canvas.height),
        )
-        row_height = int(row_height * scale_up_percent)
-        _, _, scaled_layout = map_layout(camera_layout, row_height)
+        row_height = max(1, int(row_height * scale_up_percent))
+        _, _, scaled_layout = map_layout(row_height)

        if scaled_layout:
            return scaled_layout
@@ -745,8 +733,7 @@ class BirdsEyeFrameManager:
    def update(
        self,
        camera: str,
-        object_count: int,
-        motion_count: int,
+        activity: BirdseyeActivity,
        frame_time: float,
        frame: np.ndarray,
    ) -> tuple[bool, bool]:
@@ -774,7 +761,10 @@ class BirdsEyeFrameManager:
        # update the last active frame for the camera
        self.cameras[camera]["current_frame"] = frame.copy()
        self.cameras[camera]["current_frame_time"] = frame_time
-        if self.camera_active(camera_config.birdseye.mode, object_count, motion_count):
+        if self.camera_active(
+            camera_config.birdseye.mode,
+            activity,
+        ):
            self.cameras[camera]["last_active_frame"] = frame_time

        now = datetime.datetime.now().timestamp()
@@ -882,10 +872,29 @@ class Birdseye:
        frame_time: float,
        frame: np.ndarray,
    ) -> None:
+        has_active_object = False
+        has_stationary_object = False
+        for tracked_object in current_tracked_objects:
+            if tracked_object["stationary"]:
+                if not tracked_object["false_positive"]:
+                    has_stationary_object = True
+            else:
+                # Preserve the existing objects activity behavior, which includes
+                # non-stationary trackers before they are confirmed.
+                has_active_object = True
+
+            if has_active_object and has_stationary_object:
+                break
+
+        activity = BirdseyeActivity(
+            has_active_object=has_active_object,
+            has_stationary_object=has_stationary_object,
+            has_motion=bool(motion_boxes),
+        )
+
        frame_changed, frame_layout_changed = self.birdseye_manager.update(
            camera,
-            len([o for o in current_tracked_objects if not o["stationary"]]),
-            len(motion_boxes),
+            activity,
            frame_time,
            frame,
        )
@@ -36,9 +36,8 @@ from frigate.util.time import is_current_hour
 logger = logging.getLogger(__name__)


-DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS = "-an"
 DEFAULT_TIME_LAPSE_FFMPEG_ARGS = "-vf setpts=0.04*PTS -r 30"
-TIMELAPSE_DATA_INPUT_ARGS = "-skip_frame nokey"
+TIMELAPSE_DATA_INPUT_ARGS = "-an -skip_frame nokey"

 # Matches the setpts factor used in timelapse exports (e.g. setpts=0.04*PTS).
 # Captures the floating-point factor so we can scale expected duration.
@@ -738,7 +737,7 @@ class RecordingExporter(threading.Thread):
                parse_preset_hardware_acceleration_encode(
                    self.config.ffmpeg.ffmpeg_path,
                    hwaccel_args,
-                    f"{self.ffmpeg_input_args} {ffmpeg_input}".strip(),
+                    f"{self.ffmpeg_input_args} -an {ffmpeg_input}".strip(),
                    f"{self.ffmpeg_output_args} -movflags +faststart".strip(),
                    EncodeTypeEnum.timelapse,
                )
@@ -388,22 +388,6 @@ class RecordingMaintainer(threading.Thread):
                RecordingsDataTypeEnum.valid.value,
            )

-        # assume that empty means the relevant recording info has not been received yet
-        camera_info = self.object_recordings_info[camera]
-        most_recently_processed_frame_time = (
-            camera_info[-1][0] if len(camera_info) > 0 else 0
-        )
-
-        # ensure delayed segment info does not lead to lost segments, every
-        # retention decision below depends on complete stats for the segment
-        if (
-            datetime.datetime.fromtimestamp(
-                most_recently_processed_frame_time
-            ).astimezone(datetime.UTC)
-            < end_time
-        ):
-            return None
-
        record_config = self.config.cameras[camera].record
        segment_stats: SegmentInfo | None = None
        highest = None
@@ -417,22 +401,37 @@ class RecordingMaintainer(threading.Thread):
        # we should first just check if this segment matches that
        # and avoid any DB calls
        if highest is not None:
-            record_mode = (
-                RetainModeEnum.all if highest == "continuous" else RetainModeEnum.motion
+            # assume that empty means the relevant recording info has not been received yet
+            camera_info = self.object_recordings_info[camera]
+            most_recently_processed_frame_time = (
+                camera_info[-1][0] if len(camera_info) > 0 else 0
            )
-            segment_stats = self.segment_stats(camera, start_time, end_time)

-            # Here we only check if we should move the segment based on non-object recording retention
-            # we will always want to check for overlapping review items below before dropping the segment
-            if not segment_stats.should_discard_segment(record_mode):
-                return await self.move_segment(
-                    camera,
-                    start_time,
-                    end_time,
-                    duration,
-                    cache_path,
-                    segment_stats,
+            # ensure delayed segment info does not lead to lost segments
+            if (
+                datetime.datetime.fromtimestamp(
+                    most_recently_processed_frame_time
+                ).astimezone(datetime.UTC)
+                >= end_time
+            ):
+                record_mode = (
+                    RetainModeEnum.all
+                    if highest == "continuous"
+                    else RetainModeEnum.motion
                )
+                segment_stats = self.segment_stats(camera, start_time, end_time)
+
+                # Here we only check if we should move the segment based on non-object recording retention
+                # we will always want to check for overlapping review items below before dropping the segment
+                if not segment_stats.should_discard_segment(record_mode):
+                    return await self.move_segment(
+                        camera,
+                        start_time,
+                        end_time,
+                        duration,
+                        cache_path,
+                        segment_stats,
+                    )

        # we fell through the continuous / motion check, so we need to check the review items
        # if the cached segment overlaps with the review items:
@@ -488,6 +487,10 @@ class RecordingMaintainer(threading.Thread):
        # continuous/motion retention (either disabled or segment_stats said
        # discard), so waiting longer just fills the cache.
        else:
+            camera_info = self.object_recordings_info[camera]
+            most_recently_processed_frame_time = (
+                camera_info[-1][0] if len(camera_info) > 0 else 0
+            )
            retain_cutoff = datetime.datetime.fromtimestamp(
                most_recently_processed_frame_time - record_config.event_pre_capture
            ).astimezone(datetime.UTC)
@@ -476,7 +476,7 @@ class ReviewSegmentMaintainer(threading.Thread):

                if not object["sub_label"]:
                    segment.detections[object["id"]] = object["label"]
-                elif object["sub_label"][0] in self.config.model.all_attributes:
+                elif object["sub_label"][0] in self.config.all_attributes:
                    segment.detections[object["id"]] = object["sub_label"][0]
                else:
                    segment.detections[object["id"]] = f"{object['label']}-verified"
@@ -614,7 +614,7 @@ class ReviewSegmentMaintainer(threading.Thread):
            for object in activity.get_all_objects():
                if not object["sub_label"]:
                    detections[object["id"]] = object["label"]
-                elif object["sub_label"][0] in self.config.model.all_attributes:
+                elif object["sub_label"][0] in self.config.all_attributes:
                    detections[object["id"]] = object["sub_label"][0]
                else:
                    detections[object["id"]] = f"{object['label']}-verified"
@@ -322,19 +322,20 @@ async def set_gpu_stats(
 async def set_npu_usages(config: FrigateConfig, all_stats: dict[str, Any]) -> None:
    stats: dict[str, dict] = {}

-    for detector in config.detectors.values():
-        if detector.type == "rknn":
-            # Rockchip NPU usage
-            rk_usage = get_rockchip_npu_stats()
-            stats["rockchip"] = rk_usage
-        elif detector.type == "openvino" and detector.device == "NPU":
-            # OpenVINO NPU usage
-            ov_usage = get_openvino_npu_stats()
-            stats["openvino"] = ov_usage
-        elif detector.type == "axengine":
-            # AXERA NPU usage
-            axcl_usage = get_axcl_npu_stats()
-            stats["axengine"] = axcl_usage
+    for model in config.models:
+        for device in config.devices_for_model(model):
+            if device.detector == "rknn":
+                # Rockchip NPU usage
+                rk_usage = get_rockchip_npu_stats()
+                stats["rockchip"] = rk_usage
+            elif device.detector == "openvino" and device.device == "NPU":
+                # OpenVINO NPU usage
+                ov_usage = get_openvino_npu_stats()
+                stats["openvino"] = ov_usage
+            elif device.detector == "axengine":
+                # AXERA NPU usage
+                axcl_usage = get_axcl_npu_stats()
+                stats["axengine"] = axcl_usage

    if stats:
        all_stats["npu_usages"] = stats
@@ -352,9 +353,8 @@ def stats_snapshot(
    total_camera_fps = total_process_fps = total_skipped_fps = total_detection_fps = 0

    stats["cameras"] = {}
-    for name, camera_stats in list(camera_metrics.items()):
-        camera_config = config.cameras.get(name)
-        if camera_config is None:
+    for name, camera_stats in camera_metrics.items():
+        if name not in config.cameras:
            continue

        total_camera_fps += camera_stats.camera_fps.value
@@ -371,7 +371,7 @@ def stats_snapshot(
        # Calculate connection quality based on current state
        # This is computed at stats-collection time so offline cameras
        # correctly show as unusable rather than excellent
-        expected_fps = camera_config.detect.fps
+        expected_fps = config.cameras[name].detect.fps
        current_fps = camera_stats.camera_fps.value
        reconnects = camera_stats.reconnects_last_hour.value
        stalls = camera_stats.stalls_last_hour.value
@@ -399,7 +399,7 @@ def stats_snapshot(
            "process_fps": round(camera_stats.process_fps.value, 2),
            "skipped_fps": round(camera_stats.skipped_fps.value, 2),
            "detection_fps": round(camera_stats.detection_fps.value, 2),
-            "detection_enabled": camera_config.detect.enabled,
+            "detection_enabled": config.cameras[name].detect.enabled,
            "pid": pid,
            "capture_pid": capture_pid,
            "ffmpeg_pid": ffmpeg_pid,
@@ -144,9 +144,7 @@ class BaseTestHttp(unittest.TestCase):
        except OSError:
            pass

-    def create_app(
-        self, stats=None, event_metadata_publisher=None, enforce_default_admin=False
-    ):
+    def create_app(self, stats=None, event_metadata_publisher=None):
        from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user

        app = create_fastapi_app(
@@ -160,7 +158,7 @@ class BaseTestHttp(unittest.TestCase):
            event_metadata_publisher,
            None,
            DebugReplayManager(),
-            enforce_default_admin=enforce_default_admin,
+            enforce_default_admin=False,
        )

        # Default test mocks for authentication
@@ -2,7 +2,6 @@

 from unittest.mock import patch

-from frigate.jobs.debug_replay import NoRecordingsError
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp

@@ -67,32 +66,6 @@ class TestDebugReplayAPI(BaseTestHttp):
        # (CodeQL: information exposure through an exception).
        self.assertEqual(body["message"], "Invalid debug replay parameters")

-    def test_start_returns_404_when_no_recordings(self):
-        with patch(
-            "frigate.api.debug_replay.start_debug_replay_job",
-            side_effect=NoRecordingsError(
-                "No recordings found for camera 'front' in the specified time range"
-            ),
-        ):
-            with AuthTestClient(self.app) as client:
-                resp = client.post(
-                    "/debug_replay/start",
-                    json={
-                        "camera": "front",
-                        "start_time": 100,
-                        "end_time": 200,
-                    },
-                )
-
-        self.assertEqual(resp.status_code, 404)
-        body = resp.json()
-        self.assertFalse(body["success"])
-        # Message is hard-coded so we don't echo exception text back to clients
-        # (CodeQL: information exposure through an exception).
-        self.assertEqual(
-            body["message"], "No recordings found in the selected time range"
-        )
-
    def test_start_returns_409_when_session_already_active(self):
        with patch(
            "frigate.api.debug_replay.start_debug_replay_job",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Nicolas Mowen	1831c5818a	Refactor detector and model management	2026-08-14 14:00:00 -06:00
Ersa Oktavian RamadanandJosh Hawkins	74c932c3f5	Refactor Birdseye activity types as composable booleans (#23940 ) * Add combined motion and object Birdseye mode Add a motion_objects mode that keeps Birdseye active when motion is detected or a confirmed tracked object is present, including stationary objects. Wire the mode through configuration, runtime commands, API schemas, documentation, and UI labels. Exclude false-positive trackers and add regression coverage for Birdseye activation and MQTT validation. * Refactor Birdseye activity types as booleans Replace combination-specific Birdseye modes with composable boolean activity types for motion, active objects, stationary objects, and continuous display. Preserve legacy single-mode configuration and MQTT inputs, support canonical comma-separated MQTT combinations, and allow scalar YAML values to be replaced by nested settings through the config API. * Preserve OpenVINO config translations Regenerate the configuration translations with the OpenVINO detector schema available so the unrelated production detector labels remain intact. * Preserve partial Birdseye mode overrides Allow an empty activity selection with a canonical NONE MQTT state so partial camera and profile overrides can disable inherited flags without failing validation. Add regression coverage for camera and profile inheritance, document the NONE contract, and keep the generated schema fixture scoped to Birdseye. * Address Birdseye activity review feedback Move scalar mode compatibility into the 0.18-1 config migration and reject empty activity selections instead of publishing a NONE state. Pass activity signals through a frozen dataclass, preserve existing active-object tracker behavior, and require confirmed stationary objects. Revert the generic YAML mutation and cover migration, inheritance, MQTT, and activation regressions. * Move Birdseye migration to 0.19 Use the 0.19-0 configuration revision for converting scalar Birdseye modes to composable activity flags, and update the migration regression coverage accordingly. * Remove Birdseye migration test Drop the dedicated config migration test as requested during review while retaining the 0.19-0 migration implementation.	2026-08-14 10:05:08 -05:00
Josh Hawkins	a0042b8d7f	Fix birdseye layout overlap with mixed landscape/portrait cameras (#22917 ) * fix birdseye layout calculation replace the two pass layout with a single pass pixel space algorithm * add test	2026-08-14 10:05:08 -05:00
Nicolas MowenandJosh Hawkins	659d26658b	Don't require object type for parameter in categorized names tool	2026-08-14 10:05:08 -05:00
Filious Louis Josh Hawkins Filious Louis	6c47bbfbbc	Dynamically resolve Intel NPU (#23761 ) * Add support for newer Intel NPU busy time counter * Resolve Intel NPU device dynamically --------- Co-authored-by: Filious Louis <1417132+fjlouis@users.noreply.github.com>	2026-08-14 10:05:08 -05:00
DoFabienandJosh Hawkins	c4d53484b7	Improve recording timeline and VOD query performance (#23862 ) * Improve recording timeline and VOD query performance * Add recording query boundary tests	2026-08-14 10:05:08 -05:00
Nicolas MowenandJosh Hawkins	928dac506a	GenAI Chat Prompt Refinements (#23864 ) * Prompt refactoring and optimization * Update spec	2026-08-14 10:05:08 -05:00
Nicolas MowenandJosh Hawkins	c1bde8ca20	Update to 0.19	2026-08-14 10:05:08 -05:00