[docs] fix topic for camera status (#21462)

* Use thread lock for JinaV2 call as it sets multiple internal fields while being called

* fix audio label translation in explore filter

* Show event in all cases, even without non-none match

* improve i18n key fallback when translation files aren't loaded

just display a valid time now instead of "invalid time"

---------

Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>

.github/workflows/pull_request.yml

@@ -19,9 +19,9 @@ jobs:
       - uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@master
+      - uses: actions/setup-node@v6
         with:
-          node-version: 16.x
+          node-version: 20.x
       - run: npm install
         working-directory: ./web
       - name: Lint
@@ -35,7 +35,7 @@ jobs:
       - uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@master
+      - uses: actions/setup-node@v6
         with:
           node-version: 20.x
       - run: npm install
@@ -78,7 +78,7 @@ jobs:
         uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@master
+      - uses: actions/setup-node@v6
         with:
           node-version: 20.x
       - name: Install devcontainer cli

README_CN.md

@@ -4,14 +4,14 @@
 
 # Frigate NVR™ - 一个具有实时目标检测的本地 NVR
 
-[English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
-
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-
 <a href="https://hosted.weblate.org/engage/frigate-nvr/-/zh_Hans/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/-/zh_Hans/svg-badge.svg" alt="翻译状态" />
 </a>
 
+[English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
 一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备 AI 目标/物体检测功能。使用 OpenCV 和 TensorFlow 在本地为 IP 摄像头执行实时物体检测。
 
 强烈推荐使用 GPU 或者 AI 加速器（例如[Google Coral 加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)等）。它们的运行效率远远高于现在的顶级 CPU，并且功耗也极低。
@@ -38,6 +38,7 @@
 ## 协议
 
 本项目采用 **MIT 许可证**授权。
+
 **代码部分**：本代码库中的源代码、配置文件和文档均遵循 [MIT 许可证](LICENSE)。您可以自由使用、修改和分发这些代码，但必须保留原始版权声明。
 
 **商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate LLC 的商标**，**不在** MIT 许可证覆盖范围内。

docker/main/Dockerfile

@@ -237,8 +237,18 @@ ENV PYTHONWARNINGS="ignore:::numpy.core.getlimits"
 # Set HailoRT to disable logging
 ENV HAILORT_LOGGER_PATH=NONE
 
-# TensorFlow error only
+# TensorFlow C++ logging suppression (must be set before import)
+# TF_CPP_MIN_LOG_LEVEL: 0=all, 1=INFO+, 2=WARNING+, 3=ERROR+ (we use 3 for errors only)
 ENV TF_CPP_MIN_LOG_LEVEL=3
+# Suppress verbose logging from TensorFlow C++ code
+ENV TF_CPP_MIN_VLOG_LEVEL=3
+# Disable oneDNN optimization messages ("optimized with oneDNN...")
+ENV TF_ENABLE_ONEDNN_OPTS=0
+# Suppress AutoGraph verbosity during conversion
+ENV AUTOGRAPH_VERBOSITY=0
+# Google Logging (GLOG) suppression for TensorFlow components
+ENV GLOG_minloglevel=3
+ENV GLOG_logtostderr=0
 
 ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -55,7 +55,7 @@ function setup_homekit_config() {
 
     if [[ ! -f "${config_path}" ]]; then
         echo "[INFO] Creating empty HomeKit config file..."
-        echo '{}' > "${config_path}"
+        echo 'homekit: {}' > "${config_path}"
     fi
 
     # Convert YAML to JSON for jq processing
@@ -70,12 +70,14 @@ function setup_homekit_config() {
     jq '
         # Keep only the homekit section if it exists, otherwise empty object
         if has("homekit") then {homekit: .homekit} else {homekit: {}} end
-    ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || echo '{"homekit": {}}' > "${cleaned_json}"
+    ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || {
+        echo '{"homekit": {}}' > "${cleaned_json}"
+    }
 
     # Convert back to YAML and write to the config file
     yq eval -P "${cleaned_json}" > "${config_path}" 2>/dev/null || {
         echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
-        echo '{"homekit": {}}' > "${config_path}"
+        echo 'homekit: {}' > "${config_path}"
     }
 
     # Clean up temp files

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

@@ -18,6 +18,10 @@ proxy_set_header X-Forwarded-User $http_x_forwarded_user;
 proxy_set_header X-Forwarded-Groups $http_x_forwarded_groups;
 proxy_set_header X-Forwarded-Email $http_x_forwarded_email;
 proxy_set_header X-Forwarded-Preferred-Username $http_x_forwarded_preferred_username;
+proxy_set_header X-Auth-Request-User $http_x_auth_request_user;
+proxy_set_header X-Auth-Request-Groups $http_x_auth_request_groups;
+proxy_set_header X-Auth-Request-Email $http_x_auth_request_email;
+proxy_set_header X-Auth-Request-Preferred-Username $http_x_auth_request_preferred_username;
 proxy_set_header X-authentik-username $http_x_authentik_username;
 proxy_set_header X-authentik-groups $http_x_authentik_groups;
 proxy_set_header X-authentik-email $http_x_authentik_email;

docs/docs/configuration/authentication.md

@@ -270,3 +270,42 @@ To use role-based access control, you must connect to Frigate via the **authenti
 1. Log in as an **admin** user via port `8971`.
 2. Navigate to **Settings > Users**.
 3. Edit a user’s role by selecting **admin** or **viewer**.
+
+## API Authentication Guide
+
+### Getting a Bearer Token
+
+To use the Frigate API, you need to authenticate first. Follow these steps to obtain a Bearer token:
+
+#### 1. Login
+
+Make a POST request to `/login` with your credentials:
+
+```bash
+curl -i -X POST https://frigate_ip:8971/api/login \
+  -H "Content-Type: application/json" \
+  -d '{"user": "admin", "password": "your_password"}'
+```
+
+:::note
+
+You may need to include `-k` in the argument list in these steps (eg: `curl -k -i -X POST ...`) if your Frigate instance is using a self-signed certificate.
+
+:::
+
+The response will contain a cookie with the JWT token.
+
+#### 2. Using the Bearer Token
+
+Once you have the token, include it in the Authorization header for subsequent requests:
+
+```bash
+curl -H "Authorization: Bearer <your_token>" https://frigate_ip:8971/api/profile
+```
+
+#### 3. Token Lifecycle
+
+- Tokens are valid for the configured session length
+- Tokens are automatically refreshed when you visit the `/auth` endpoint
+- Tokens are invalidated when the user's password is changed
+- Use `/logout` to clear your session cookie

docs/docs/configuration/custom_classification/object_classification.md

@@ -3,7 +3,7 @@ id: object_classification
 title: Object Classification
 ---
 
-Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object.
+Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object. Classification results are visible in the Tracked Object Details pane in Explore, through the `frigate/tracked_object_details` MQTT topic, in Home Assistant sensors via the official Frigate integration, or through the event endpoints in the HTTP API.
 
 ## Minimum System Requirements
 
@@ -11,6 +11,8 @@ Object classification models are lightweight and run very fast on CPU. Inference
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 
+A CPU with AVX instructions is required for training and inference.
+
 ## Classes
 
 Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
@@ -31,9 +33,15 @@ For object classification:
   - Example: `cat` → `Leo`, `Charlie`, `None`.
 
 - **Attribute**:
-  - Added as metadata to the object (visible in /events): `<model_name>: <predicted_value>`.
+  - Added as metadata to the object, visible in the Tracked Object Details pane in Explore, `frigate/events` MQTT messages, and the HTTP API response as `<model_name>: <predicted_value>`.
   - Ideal when multiple attributes can coexist independently.
-  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
+  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not, and if they are wearing a yellow vest or not.
+
+:::note
+
+A tracked object can only have a single sub label. If you are using Triggers or Face Recognition and you configure an object classification model for `person` using the sub label type, your sub label may not be assigned correctly as it depends on which enrichment completes its analysis first. Consider using the `attribute` type instead.
+
+:::
 
 ## Assignment Requirements
 
@@ -73,13 +81,17 @@ classification:
         classification_type: sub_label # or: attribute
 ```
 
+An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For object classification models, the default is 200.
+
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
 
 ### Step 1: Name and Define
 
-Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Include a `none` class for objects that don't fit any specific category.
+Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Frigate will automatically include a `none` class for objects that don't fit any specific category.
+
+For example: To classify your two cats, create a model named "Our Cats" and create two classes, "Charlie" and "Leo". A third class, "none", will be created automatically for other neighborhood cats that are not your own.
 
 ### Step 2: Assign Training Examples
 
@@ -87,6 +99,8 @@ The system will automatically generate example images from detected objects matc
 
 When choosing which objects to classify, start with a small number of visually distinct classes and ensure your training samples match camera viewpoints and distances typical for those objects.
 
+If examples for some of your classes do not appear in the grid, you can continue configuring the model without them. New images will begin to appear in the Recent Classifications view. When your missing classes are seen, classify them from this view and retrain your model.
+
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
@@ -94,3 +108,23 @@ When choosing which objects to classify, start with a small number of visually d
 - **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
 - **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
 - **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
+
+## Debugging Classification Models
+
+To troubleshoot issues with object classification models, enable debug logging to see detailed information about classification attempts, scores, and consensus calculations.
+
+Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
+
+```yaml
+logger:
+  default: info
+  logs:
+    frigate.data_processing.real_time.custom_classification: debug
+```
+
+The debug logs will show:
+
+- Classification probabilities for each attempt
+- Whether scores meet the threshold requirement
+- Consensus calculations and when assignments are made
+- Object classification history and weighted scores

docs/docs/configuration/custom_classification/state_classification.md

@@ -3,7 +3,7 @@ id: state_classification
 title: State Classification
 ---
 
-State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region.
+State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region. Classification results are available through the `frigate/<camera_name>/classification/<model_name>` MQTT topic and in Home Assistant sensors via the official Frigate integration.
 
 ## Minimum System Requirements
 
@@ -11,6 +11,8 @@ State classification models are lightweight and run very fast on CPU. Inference
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 
+A CPU with AVX instructions is required for training and inference.
+
 ## Classes
 
 Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.
@@ -46,6 +48,8 @@ classification:
             crop: [0, 180, 220, 400]
 ```
 
+An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100.
+
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
@@ -60,11 +64,9 @@ Choose one or more cameras and draw a rectangle over the area of interest for ea
 
 ### Step 3: Assign Training Examples
 
-The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state.
+The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state. It's not strictly required to select all images you see. If a state is missing from the samples, you can train it from the Recent tab later.
 
-**Important**: All images must be assigned to a state before training can begin. This includes images that may not be optimal, such as when people temporarily block the view, sun glare is present, or other distractions occur. Assign these images to the state that is actually present (based on what you know the state to be), not based on the distraction. This training helps the model correctly identify the state even when such conditions occur during inference.
-
-Once all images are assigned, training will begin automatically.
+Once some images are assigned, training will begin automatically.
 
 ### Improving the Model
 
@@ -72,3 +74,34 @@ Once all images are assigned, training will begin automatically.
 - **Data collection**: Use the model's Recent Classifications tab to gather balanced examples across times of day and weather.
 - **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
 - **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.
+
+## Debugging Classification Models
+
+To troubleshoot issues with state classification models, enable debug logging to see detailed information about classification attempts, scores, and state verification.
+
+Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
+
+```yaml
+logger:
+  default: info
+  logs:
+    frigate.data_processing.real_time.custom_classification: debug
+```
+
+The debug logs will show:
+
+- Classification probabilities for each attempt
+- Whether scores meet the threshold requirement
+- State verification progress (consecutive detections needed)
+- When state changes are published
+
+### Recent Classifications
+
+For state classification, images are only added to recent classifications under specific circumstances:
+
+- **First detection**: The first classification attempt for a camera is always saved
+- **State changes**: Images are saved when the detected state differs from the current verified state
+- **Pending verification**: Images are saved when there's a pending state change being verified (requires 3 consecutive identical states)
+- **Low confidence**: Images with scores below 100% are saved even if the state matches the current state (useful for training)
+
+Images are **not** saved when the state is stable (detected state matches current state) **and** the score is 100%. This prevents unnecessary storage of redundant high-confidence classifications.

docs/docs/configuration/genai.md

@@ -56,7 +56,7 @@ Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
 
 :::note
 
@@ -64,6 +64,10 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
 
 :::
 
+#### Ollama Cloud models
+
+Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
+
 ### Configuration
 
 ```yaml

docs/docs/configuration/genai/review_summaries.md

@@ -16,12 +16,13 @@ Review summaries provide structured JSON responses that are saved for each revie
 ```
 - `title` (string): A concise, direct title that describes the purpose or overall action (e.g., "Person taking out trash", "Joe walking dog").
 - `scene` (string): A narrative description of what happens across the sequence from start to finish, including setting, detected objects, and their observable actions.
+- `shortSummary` (string): A brief 2-sentence summary of the scene, suitable for notifications. This is a condensed version of the scene description.
 - `confidence` (float): 0-1 confidence in the analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous.
 - `other_concerns` (list): List of user-defined concerns that may need additional investigation.
 - `potential_threat_level` (integer): 0, 1, or 2 as defined below.
 ```
 
-This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will also automatically show the title and description when the data is available.
+This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will automatically show the title and `shortSummary` when the data is available, while the full `scene` description is available in the UI for detailed review.
 
 ### Defining Typical Activity
 

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -13,7 +13,7 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
 
 - **AMD**
 
-  - ROCm will automatically be detected and used for enrichments in the `-rocm` Frigate image.
+  - ROCm support in the `-rocm` Frigate image is automatically detected for enrichments, but only some enrichment models are available due to ROCm's focus on LLMs and limited stability with certain neural network models. Frigate disables models that perform poorly or are unstable to ensure reliable operation, so only compatible enrichments may be active.
 
 - **Intel**
 

docs/docs/configuration/object_detectors.md

@@ -146,16 +146,16 @@ detectors:
 
 ### EdgeTPU Supported Models
 
-| Model                                 | Notes                                       |
-| ------------------------------------- | ------------------------------------------- |
-| [MobileNet v2](#ssdlite-mobilenet-v2) | Default model                               |
-| [YOLOv9](#yolo-v9)                    | More accurate but slower than default model |
+| Model                   | Notes                                       |
+| ----------------------- | ------------------------------------------- |
+| [Mobiledet](#mobiledet) | Default model                               |
+| [YOLOv9](#yolov9)       | More accurate but slower than default model |
 
-#### SSDLite MobileNet v2
+#### Mobiledet
 
 A TensorFlow Lite model is provided in the container at `/edgetpu_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`.
 
-#### YOLO v9
+#### YOLOv9
 
 [YOLOv9](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite) models that are compiled for Tensorflow Lite and properly quantized are supported, but not included by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`. Note that the model may require a custom label file (eg. [use this 17 label file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) for the model linked above.)
 
@@ -175,7 +175,7 @@ model:
   width: 320 # <--- should match the imgsize of the model, typically 320
   height: 320 # <--- should match the imgsize of the model, typically 320
   path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
-  labelmap_path: /labelmap/labels-coco-17.txt
+  labelmap_path: /config/labels-coco17.txt
 ```
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 17 objects.

docs/docs/frigate/updating.md

@@ -5,7 +5,7 @@ title: Updating
 
 # Updating Frigate
 
-The current stable version of Frigate is **0.16.2**. 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/v0.16.2).
+The current stable version of Frigate is **0.17.0**. 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/v0.17.0).
 
 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 Addon, etc.). Below are instructions for the most common setups.
 
@@ -33,21 +33,21 @@ 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., `0.16.2` instead of `0.15.2`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.17.0` instead of `0.16.3`). For example:
        ```yaml
        services:
          frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.16.2
+           image: ghcr.io/blakeblackshear/frigate:0.17.0
        ```
      - Then pull the image:
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
+       docker pull ghcr.io/blakeblackshear/frigate:0.17.0
        ```
      - **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., `0.16.2`, `0.16.2-tensorrt`, or `stable`):
+     - Pull the image with the appropriate tag (e.g., `0.17.0`, `0.17.0-tensorrt`, or `stable`):
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
+       docker pull ghcr.io/blakeblackshear/frigate:0.17.0
        ```
 
 3. **Start the Container**:
@@ -105,8 +105,8 @@ If an update causes issues:
 1. Stop Frigate.
 2. Restore your backed-up config file and database.
 3. Revert to the previous image version:
-   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`) in your `docker run` command.
-   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`), and re-run `docker compose up -d`.
+   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.3`) in your `docker run` command.
+   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.3`), and re-run `docker compose up -d`.
    - For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
 4. Verify the old version is running again.
 

docs/docs/integrations/home-assistant.md

@@ -245,6 +245,12 @@ To load a preview gif of a review item:
 https://HA_URL/api/frigate/notifications/<review-id>/review_preview.gif
 ```
 
+To load the thumbnail of a review item:
+
+```
+https://HA_URL/api/frigate/notifications/<review-id>/<camera>/review_thumbnail.webp
+```
+
 <a name="streams"></a>
 
 ## RTSP stream

docs/docs/integrations/mqtt.md

@@ -280,7 +280,7 @@ Topic with current state of notifications. Published values are `ON` and `OFF`.
 
 ## Frigate Camera Topics
 
-### `frigate/<camera_name>/<role>/status`
+### `frigate/<camera_name>/status/<role>`
 
 Publishes the current health status of each role that is enabled (`audio`, `detect`, `record`). Possible values are:
 

docs/docs/integrations/third_party_extensions.md

@@ -38,3 +38,7 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
 ## [Periscope](https://github.com/maksz42/periscope)
 
 [Periscope](https://github.com/maksz42/periscope) is a lightweight Android app that turns old devices into live viewers for Frigate. It works on Android 2.2 and above, including Android TV. It supports authentication and HTTPS.
+
+## [Scrypted - Frigate bridge plugin](https://github.com/apocaliss92/scrypted-frigate-bridge)
+
+[Scrypted - Frigate bridge](https://github.com/apocaliss92/scrypted-frigate-bridge) is an plugin that allows to ingest Frigate detections, motion, videoclips on Scrypted as well as provide templates to export rebroadcast configurations on Frigate.

docs/docs/plus/index.md

@@ -15,13 +15,11 @@ There are three model types offered in Frigate+, `mobiledet`, `yolonas`, and `yo
 
 Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
 
-| Model Type  | Description                                                                                                                                                                                                                        |
-| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                                                                                                  |
-| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                                                                                       |
-| `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX\*, Apple Silicon\*, and Rockchip NPUs. |
-
-_\* Support coming in 0.17_
+| Model Type  | Description                                                                                                                                                                                                                    |
+| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                                                                                              |
+| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                                                                                   |
+| `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX, Apple Silicon, and Rockchip NPUs. |
 
 ### YOLOv9 Details
 
@@ -39,7 +37,7 @@ If you have a Hailo device, you will need to specify the hardware you have when
 
 #### Rockchip (RKNN) Support
 
-For 0.16, YOLOv9 onnx models will need to be manually converted. First, you will need to configure Frigate to use the model id for your YOLOv9 onnx model so it downloads the model to your `model_cache` directory. From there, you can follow the [documentation](/configuration/object_detectors.md#converting-your-own-onnx-model-to-rknn-format) to convert it. Automatic conversion is coming in 0.17.
+For 0.16, YOLOv9 onnx models will need to be manually converted. First, you will need to configure Frigate to use the model id for your YOLOv9 onnx model so it downloads the model to your `model_cache` directory. From there, you can follow the [documentation](/configuration/object_detectors.md#converting-your-own-onnx-model-to-rknn-format) to convert it. Automatic conversion is available in 0.17 and later.
 
 ## Supported detector types
 
@@ -55,7 +53,7 @@ Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVi
 | [Hailo8/Hailo8L/Hailo8R](/configuration/object_detectors#hailo-8)                | `hailo8l`                 | `yolov9`               |
 | [Rockchip NPU](/configuration/object_detectors#rockchip-platform)\*              | `rknn`                    | `yolov9`               |
 
-_\* Requires manual conversion in 0.16. Automatic conversion coming in 0.17._
+_\* Requires manual conversion in 0.16. Automatic conversion available in 0.17 and later._
 
 ## Improving your model
 

docs/docs/troubleshooting/dummy-camera.md

@@ -0,0 +1,60 @@
+---
+id: dummy-camera
+title: Troubleshooting Detection
+---
+
+When investigating object detection or tracking problems, it can be helpful to replay an exported video as a temporary "dummy" camera. This lets you reproduce issues locally, iterate on configuration (detections, zones, enrichment settings), and capture logs and clips for analysis.
+
+## When to use
+
+- Replaying an exported clip to reproduce incorrect detections
+- Testing configuration changes (model settings, trackers, filters) against a known clip
+- Gathering deterministic logs and recordings for debugging or issue reports
+
+## Example Config
+
+Place the clip you want to replay in a location accessible to Frigate (for example `/media/frigate/` or the repository `debug/` folder when developing). Then add a temporary camera to your `config/config.yml` like this:
+
+```yaml
+cameras:
+  test:
+    ffmpeg:
+      inputs:
+        - path: /media/frigate/car-stopping.mp4
+          input_args: -re -stream_loop -1 -fflags +genpts
+          roles:
+            - detect
+    detect:
+      enabled: true
+    record:
+      enabled: false
+    snapshots:
+      enabled: false
+```
+
+- `-re -stream_loop -1` tells `ffmpeg` to play the file in realtime and loop indefinitely, which is useful for long debugging sessions.
+- `-fflags +genpts` helps generate presentation timestamps when they are missing in the file.
+
+## Steps
+
+1. Export or copy the clip you want to replay to the Frigate host (e.g., `/media/frigate/` or `debug/clips/`).
+2. Add the temporary camera to `config/config.yml` (example above). Use a unique name such as `test` or `replay_camera` so it's easy to remove later.
+   - If you're debugging a specific camera, copy the settings from that camera (frame rate, model/enrichment settings, zones, etc.) into the temporary camera so the replay closely matches the original environment. Leave `record` and `snapshots` disabled unless you are specifically debugging recording or snapshot behavior.
+3. Restart Frigate.
+4. Observe the Debug view in the UI and logs as the clip is replayed. Watch detections, zones, or any feature you're looking to debug, and note any errors in the logs to reproduce the issue.
+5. Iterate on camera or enrichment settings (model, fps, zones, filters) and re-check the replay until the behavior is resolved.
+6. Remove the temporary camera from your config after debugging to avoid spurious telemetry or recordings.
+
+## Variables to consider in object tracking
+
+- The exported video will not always line up exactly with how it originally ran through Frigate (or even with the last loop). Different frames may be used on replay, which can change detections and tracking.
+- Motion detection depends on the frames used; small frame shifts can change motion regions and therefore what gets passed to the detector.
+- Object detection is not deterministic: models and post-processing can yield different results across runs, so you may not get identical detections or track IDs every time.
+
+When debugging, treat the replay as a close approximation rather than a byte-for-byte replay. Capture multiple runs, enable recording if helpful, and examine logs and saved event clips to understand variability.
+
+## Troubleshooting
+
+- No video: verify the path is correct and accessible from the Frigate process/container.
+- FFmpeg errors: check the log output for ffmpeg-specific flags and adjust `input_args` accordingly for your file/container. You may also need to disable hardware acceleration (`hwaccel_args: ""`) for the dummy camera.
+- No detections: confirm the camera `roles` include `detect`, and model/detector configuration is enabled.

docs/docs/troubleshooting/memory.md

@@ -9,8 +9,20 @@ Frigate includes built-in memory profiling using [memray](https://bloomberg.gith
 
 Memory profiling is controlled via the `FRIGATE_MEMRAY_MODULES` environment variable. Set it to a comma-separated list of module names you want to profile:
 
+```yaml
+# docker-compose example
+services:
+  frigate:
+    ...
+    environment:
+      - FRIGATE_MEMRAY_MODULES=frigate.embeddings,frigate.capture
+```
+
 ```bash
-export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
+# docker run example
+docker run -e FRIGATE_MEMRAY_MODULES="frigate.embeddings" \
+   ...
+   --name frigate <frigate_image>
 ```
 
 ### Module Names
@@ -28,7 +40,7 @@ Frigate processes are named using a module-based naming scheme. Common module na
 You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
 
 ```bash
-export FRIGATE_MEMRAY_MODULES="frigate.capture:front_door"
+FRIGATE_MEMRAY_MODULES=frigate.capture:front_door
 ```
 
 When you specify a module name (e.g., `frigate.capture`), all processes with that module prefix will be profiled. For example, `frigate.capture` will profile all camera capture processes.
@@ -55,11 +67,20 @@ After a process exits normally, you'll find HTML reports in `/config/memray_repo
 
 If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
 
+- Run `memray` inside the Frigate container:
+
 ```bash
-memray flamegraph /config/memray_reports/<module_name>.bin
+docker-compose exec frigate memray flamegraph /config/memray_reports/<module_name>.bin
+# or
+docker exec -it <container_name_or_id> memray flamegraph /config/memray_reports/<module_name>.bin
 ```
 
-This will generate an HTML file that you can open in your browser.
+- You can also copy the `.bin` file to the host and run `memray` locally if you have it installed:
+
+```bash
+docker cp <container_name_or_id>:/config/memray_reports/<module_name>.bin /tmp/
+memray flamegraph /tmp/<module_name>.bin
+```
 
 ## Understanding the Reports
 
@@ -110,20 +131,4 @@ The interactive HTML reports allow you to:
 - Check that memray is properly installed (included by default in Frigate)
 - Verify the process actually started and ran (check process logs)
 
-## Example Usage
-
-```bash
-# Enable profiling for review and capture modules
-export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
-
-# Start Frigate
-# ... let it run for a while ...
-
-# Check for reports
-ls -lh /config/memray_reports/
-
-# If a process crashed, manually generate report
-memray flamegraph /config/memray_reports/frigate_capture_front_door.bin
-```
-
 For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).

docs/sidebars.ts

@@ -132,6 +132,7 @@ const sidebars: SidebarsConfig = {
       "troubleshooting/gpu",
       "troubleshooting/edgetpu",
       "troubleshooting/memory",
+      "troubleshooting/dummy-camera",
     ],
     Development: [
       "development/contributing",

docs/static/frigate-api.yaml

@@ -14,19 +14,38 @@ paths:
     get:
       tags:
         - Auth
-      summary: Auth
+      summary: Authenticate request
+      description: |-
+        Authenticates the current request based on proxy headers or JWT token.
+        This endpoint verifies authentication credentials and manages JWT token refresh.
+        On success, no JSON body is returned; authentication state is communicated via response headers and cookies.
       operationId: auth_auth_get
       responses:
-        "200":
-          description: Successful Response
-          content:
-            application/json:
-              schema: {}
+        "202":
+          description: Authentication Accepted (no response body, different headers depending on auth method)
+          headers:
+            remote-user:
+              description: Authenticated username or "viewer" in proxy-only mode
+              schema:
+                type: string
+            remote-role:
+              description: Resolved role (e.g., admin, viewer, or custom)
+              schema:
+                type: string
+            Set-Cookie:
+              description: May include refreshed JWT cookie ("frigate-token") when applicable
+              schema:
+                type: string
+        "401":
+          description: Authentication Failed
   /profile:
     get:
       tags:
         - Auth
-      summary: Profile
+      summary: Get user profile
+      description: |-
+        Returns the current authenticated user's profile including username, role, and allowed cameras.
+        This endpoint requires authentication and returns information about the user's permissions.
       operationId: profile_profile_get
       responses:
         "200":
@@ -34,11 +53,16 @@ paths:
           content:
             application/json:
               schema: {}
+        "401":
+          description: Unauthorized
   /logout:
     get:
       tags:
         - Auth
-      summary: Logout
+      summary: Logout user
+      description: |-
+        Logs out the current user by clearing the session cookie.
+        After logout, subsequent requests will require re-authentication.
       operationId: logout_logout_get
       responses:
         "200":
@@ -46,11 +70,22 @@ paths:
           content:
             application/json:
               schema: {}
+        "303":
+          description: See Other (redirects to login page)
   /login:
     post:
       tags:
         - Auth
-      summary: Login
+      summary: Login with credentials
+      description: |-
+        Authenticates a user with username and password.
+        Returns a JWT token as a secure HTTP-only cookie that can be used for subsequent API requests.
+        The JWT token can also be retrieved from the response and used as a Bearer token in the Authorization header.
+
+        Example using Bearer token:
+        ```
+        curl -H "Authorization: Bearer <token_value>" https://frigate_ip:8971/api/profile
+        ```
       operationId: login_login_post
       requestBody:
         required: true
@@ -64,6 +99,11 @@ paths:
           content:
             application/json:
               schema: {}
+        "401":
+          description: Login Failed - Invalid credentials
+          content:
+            application/json:
+              schema: {}
         "422":
           description: Validation Error
           content:
@@ -74,7 +114,10 @@ paths:
     get:
       tags:
         - Auth
-      summary: Get Users
+      summary: Get all users
+      description: |-
+        Returns a list of all users with their usernames and roles.
+        Requires admin role. Each user object contains the username and assigned role.
       operationId: get_users_users_get
       responses:
         "200":
@@ -82,10 +125,19 @@ paths:
           content:
             application/json:
               schema: {}
+        "403":
+          description: Forbidden - Admin role required
     post:
       tags:
         - Auth
-      summary: Create User
+      summary: Create new user
+      description: |-
+        Creates a new user with the specified username, password, and role.
+        Requires admin role. Password must meet strength requirements:
+        - Minimum 8 characters
+        - At least one uppercase letter
+        - At least one digit
+        - At least one special character (!@#$%^&*(),.?":{}\|<>)
       operationId: create_user_users_post
       requestBody:
         required: true
@@ -99,6 +151,13 @@ paths:
           content:
             application/json:
               schema: {}
+        "400":
+          description: Bad Request - Invalid username or role
+          content:
+            application/json:
+              schema: {}
+        "403":
+          description: Forbidden - Admin role required
         "422":
           description: Validation Error
           content:
@@ -109,7 +168,10 @@ paths:
     delete:
       tags:
         - Auth
-      summary: Delete User
+      summary: Delete user
+      description: |-
+        Deletes a user by username. The built-in admin user cannot be deleted.
+        Requires admin role. Returns success message or error if user not found.
       operationId: delete_user_users__username__delete
       parameters:
         - name: username
@@ -118,12 +180,15 @@ paths:
           schema:
             type: string
             title: Username
+            description: The username of the user to delete
       responses:
         "200":
           description: Successful Response
           content:
             application/json:
               schema: {}
+        "403":
+          description: Forbidden - Cannot delete admin user or admin role required
         "422":
           description: Validation Error
           content:
@@ -134,7 +199,17 @@ paths:
     put:
       tags:
         - Auth
-      summary: Update Password
+      summary: Update user password
+      description: |-
+        Updates a user's password. Users can only change their own password unless they have admin role.
+        Requires the current password to verify identity for non-admin users.
+        Password must meet strength requirements:
+        - Minimum 8 characters
+        - At least one uppercase letter
+        - At least one digit
+        - At least one special character (!@#$%^&*(),.?":{}\|<>)
+
+        If user changes their own password, a new JWT cookie is automatically issued.
       operationId: update_password_users__username__password_put
       parameters:
         - name: username
@@ -143,6 +218,7 @@ paths:
           schema:
             type: string
             title: Username
+            description: The username of the user whose password to update
       requestBody:
         required: true
         content:
@@ -155,6 +231,14 @@ paths:
           content:
             application/json:
               schema: {}
+        "400":
+          description: Bad Request - Current password required or password doesn't meet requirements
+        "401":
+          description: Unauthorized - Current password is incorrect
+        "403":
+          description: Forbidden - Viewers can only update their own password
+        "404":
+          description: Not Found - User not found
         "422":
           description: Validation Error
           content:
@@ -165,7 +249,10 @@ paths:
     put:
       tags:
         - Auth
-      summary: Update Role
+      summary: Update user role
+      description: |-
+        Updates a user's role. The built-in admin user's role cannot be modified.
+        Requires admin role. Valid roles are defined in the configuration.
       operationId: update_role_users__username__role_put
       parameters:
         - name: username
@@ -174,6 +261,7 @@ paths:
           schema:
             type: string
             title: Username
+            description: The username of the user whose role to update
       requestBody:
         required: true
         content:
@@ -186,6 +274,10 @@ paths:
           content:
             application/json:
               schema: {}
+        "400":
+          description: Bad Request - Invalid role
+        "403":
+          description: Forbidden - Cannot modify admin user's role or admin role required
         "422":
           description: Validation Error
           content:
@@ -524,6 +616,32 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
+  /classification/attributes:
+    get:
+      tags:
+        - Classification
+      summary: Get custom classification attributes
+      description: |-
+        Returns custom classification attributes for a given object type.
+        Only includes models with classification_type set to 'attribute'.
+        By default returns a flat sorted list of all attribute labels.
+        If group_by_model is true, returns attributes grouped by model name.
+      operationId: get_custom_attributes_classification_attributes_get
+      parameters:
+        - name: object_type
+          in: query
+          schema:
+            type: string
+        - name: group_by_model
+          in: query
+          schema:
+            type: boolean
+            default: false
+      responses:
+        "200":
+          description: Successful Response
+        "422":
+          description: Validation Error
   /classification/{name}/dataset:
     get:
       tags:
@@ -2820,6 +2938,42 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
+  /events/{event_id}/attributes:
+    post:
+      tags:
+        - Events
+      summary: Set custom classification attributes
+      description: |-
+        Sets an event's custom classification attributes for all attribute-type
+        models that apply to the event's object type.
+            Returns a success message or an error if the event is not found.
+      operationId: set_attributes_events__event_id__attributes_post
+      parameters:
+        - name: event_id
+          in: path
+          required: true
+          schema:
+            type: string
+            title: Event Id
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              $ref: "#/components/schemas/EventsAttributesBody"
+      responses:
+        "200":
+          description: Successful Response
+          content:
+            application/json:
+              schema:
+                $ref: "#/components/schemas/GenericResponse"
+        "422":
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: "#/components/schemas/HTTPValidationError"
   /events/{event_id}/description:
     post:
       tags:
@@ -4867,6 +5021,18 @@ components:
       required:
         - subLabel
       title: EventsSubLabelBody
+    EventsAttributesBody:
+      properties:
+        attributes:
+          type: object
+          title: Attributes
+          description: Object with model names as keys and attribute values
+          additionalProperties:
+            type: string
+      type: object
+      required:
+        - attributes
+      title: EventsAttributesBody
     ExportModel:
       properties:
         id:

frigate/api/auth.py

@@ -143,17 +143,6 @@ def require_admin_by_default():
     return admin_checker
 
 
-def _is_authenticated(request: Request) -> bool:
-    """
-    Helper to determine if a request is from an authenticated user.
-
-    Returns True if the request has a valid authenticated user (not anonymous).
-    Port 5000 internal requests are considered anonymous despite having admin role.
-    """
-    username = request.headers.get("remote-user")
-    return username is not None and username != "anonymous"
-
-
 def allow_public():
     """
     Override dependency to allow unauthenticated access to an endpoint.
@@ -173,27 +162,24 @@ def allow_public():
 
 def allow_any_authenticated():
     """
-    Override dependency to allow any authenticated user (bypass admin requirement).
+    Override dependency to allow any request that passed through the /auth endpoint.
 
     Allows:
-    - Port 5000 internal requests (have admin role despite anonymous user)
-    - Any authenticated user with a real username (not "anonymous")
+    - Port 5000 internal requests (remote-user: "anonymous", remote-role: "admin")
+    - Authenticated users with JWT tokens (remote-user: username)
+    - Unauthenticated requests when auth is disabled (remote-user: "viewer")
 
     Rejects:
-    - Port 8971 requests with anonymous user (auth disabled, no proxy auth)
+    - Requests with no remote-user header (did not pass through /auth endpoint)
 
     Example:
         @router.get("/authenticated-endpoint", dependencies=[Depends(allow_any_authenticated())])
     """
 
     async def auth_checker(request: Request):
-        # Port 5000 requests have admin role and should be allowed
-        role = request.headers.get("remote-role")
-        if role == "admin":
-            return
-
-        # Otherwise require a real authenticated user (not anonymous)
-        if not _is_authenticated(request):
+        # Ensure a remote-user has been set by the /auth endpoint
+        username = request.headers.get("remote-user")
+        if username is None:
             raise HTTPException(status_code=401, detail="Authentication required")
         return
 
@@ -549,7 +535,37 @@ def resolve_role(
 
 
 # Endpoints
-@router.get("/auth", dependencies=[Depends(allow_public())])
+@router.get(
+    "/auth",
+    dependencies=[Depends(allow_public())],
+    summary="Authenticate request",
+    description=(
+        "Authenticates the current request based on proxy headers or JWT token. "
+        "This endpoint verifies authentication credentials and manages JWT token refresh. "
+        "On success, no JSON body is returned; authentication state is communicated via response headers and cookies."
+    ),
+    status_code=202,
+    responses={
+        202: {
+            "description": "Authentication Accepted (no response body)",
+            "headers": {
+                "remote-user": {
+                    "description": 'Authenticated username or "viewer" in proxy-only mode',
+                    "schema": {"type": "string"},
+                },
+                "remote-role": {
+                    "description": "Resolved role (e.g., admin, viewer, or custom)",
+                    "schema": {"type": "string"},
+                },
+                "Set-Cookie": {
+                    "description": "May include refreshed JWT cookie when applicable",
+                    "schema": {"type": "string"},
+                },
+            },
+        },
+        401: {"description": "Authentication Failed"},
+    },
+)
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
@@ -576,12 +592,12 @@ def auth(request: Request):
     # if auth is disabled, just apply the proxy header map and return success
     if not auth_config.enabled:
         # pass the user header value from the upstream proxy if a mapping is specified
-        # or use anonymous if none are specified
+        # or use viewer if none are specified
         user_header = proxy_config.header_map.user
         success_response.headers["remote-user"] = (
-            request.headers.get(user_header, default="anonymous")
+            request.headers.get(user_header, default="viewer")
             if user_header
-            else "anonymous"
+            else "viewer"
         )
 
         # parse header and resolve a valid role
@@ -689,9 +705,14 @@ def auth(request: Request):
         return fail_response
 
 
-@router.get("/profile", dependencies=[Depends(allow_any_authenticated())])
+@router.get(
+    "/profile",
+    dependencies=[Depends(allow_any_authenticated())],
+    summary="Get user profile",
+    description="Returns the current authenticated user's profile including username, role, and allowed cameras. This endpoint requires authentication and returns information about the user's permissions.",
+)
 def profile(request: Request):
-    username = request.headers.get("remote-user", "anonymous")
+    username = request.headers.get("remote-user", "viewer")
     role = request.headers.get("remote-role", "viewer")
 
     all_camera_names = set(request.app.frigate_config.cameras.keys())
@@ -703,7 +724,12 @@ def profile(request: Request):
     )
 
 
-@router.get("/logout", dependencies=[Depends(allow_public())])
+@router.get(
+    "/logout",
+    dependencies=[Depends(allow_public())],
+    summary="Logout user",
+    description="Logs out the current user by clearing the session cookie. After logout, subsequent requests will require re-authentication.",
+)
 def logout(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     response = RedirectResponse("/login", status_code=303)
@@ -714,7 +740,12 @@ def logout(request: Request):
 limiter = Limiter(key_func=get_remote_addr)
 
 
-@router.post("/login", dependencies=[Depends(allow_public())])
+@router.post(
+    "/login",
+    dependencies=[Depends(allow_public())],
+    summary="Login with credentials",
+    description='Authenticates a user with username and password. Returns a JWT token as a secure HTTP-only cookie that can be used for subsequent API requests. The JWT token can also be retrieved from the response and used as a Bearer token in the Authorization header.\n\nExample using Bearer token:\n```\ncurl -H "Authorization: Bearer <token_value>" https://frigate_ip:8971/api/profile\n```',
+)
 @limiter.limit(limit_value=rateLimiter.get_limit)
 def login(request: Request, body: AppPostLoginBody):
     JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name
@@ -752,7 +783,12 @@ def login(request: Request, body: AppPostLoginBody):
     return JSONResponse(content={"message": "Login failed"}, status_code=401)
 
 
-@router.get("/users", dependencies=[Depends(require_role(["admin"]))])
+@router.get(
+    "/users",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Get all users",
+    description="Returns a list of all users with their usernames and roles. Requires admin role. Each user object contains the username and assigned role.",
+)
 def get_users():
     exports = (
         User.select(User.username, User.role).order_by(User.username).dicts().iterator()
@@ -760,7 +796,12 @@ def get_users():
     return JSONResponse([e for e in exports])
 
 
-@router.post("/users", dependencies=[Depends(require_role(["admin"]))])
+@router.post(
+    "/users",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Create new user",
+    description='Creates a new user with the specified username, password, and role. Requires admin role. Password must meet strength requirements: minimum 8 characters, at least one uppercase letter, at least one digit, and at least one special character (!@#$%^&*(),.?":{} |<>).',
+)
 def create_user(
     request: Request,
     body: AppPostUsersBody,
@@ -789,7 +830,12 @@ def create_user(
     return JSONResponse(content={"username": body.username})
 
 
-@router.delete("/users/{username}", dependencies=[Depends(require_role(["admin"]))])
+@router.delete(
+    "/users/{username}",
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Delete user",
+    description="Deletes a user by username. The built-in admin user cannot be deleted. Requires admin role. Returns success message or error if user not found.",
+)
 def delete_user(request: Request, username: str):
     # Prevent deletion of the built-in admin user
     if username == "admin":
@@ -802,7 +848,10 @@ def delete_user(request: Request, username: str):
 
 
 @router.put(
-    "/users/{username}/password", dependencies=[Depends(allow_any_authenticated())]
+    "/users/{username}/password",
+    dependencies=[Depends(allow_any_authenticated())],
+    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 meet strength requirements: minimum 8 characters, at least one uppercase letter, at least one digit, and at least one special character (!@#$%^&*(),.?\":{} |<>). If user changes their own password, a new JWT cookie is automatically issued.",
 )
 async def update_password(
     request: Request,
@@ -830,13 +879,9 @@ async def update_password(
     except DoesNotExist:
         return JSONResponse(content={"message": "User not found"}, status_code=404)
 
-    # Require old_password when:
-    # 1. Non-admin user is changing another user's password (admin only action)
-    # 2. Any user is changing their own password
-    is_changing_own_password = current_username == username
-    is_non_admin = current_role != "admin"
-
-    if is_changing_own_password or is_non_admin:
+    # Require old_password when non-admin user is changing any password
+    # Admin users changing passwords do NOT need to provide the current password
+    if current_role != "admin":
         if not body.old_password:
             return JSONResponse(
                 content={"message": "Current password is required"},
@@ -887,6 +932,8 @@ async def update_password(
 @router.put(
     "/users/{username}/role",
     dependencies=[Depends(require_role(["admin"]))],
+    summary="Update user role",
+    description="Updates a user's role. The built-in admin user's role cannot be modified. Requires admin role. Valid roles are defined in the configuration.",
 )
 async def update_role(
     request: Request,

frigate/api/classification.py

@@ -31,6 +31,7 @@ from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
 from frigate.config.camera import DetectConfig
+from frigate.config.classification import ObjectClassificationType
 from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event
@@ -39,6 +40,7 @@ from frigate.util.classification import (
     collect_state_classification_examples,
     get_dataset_image_count,
     read_training_metadata,
+    write_training_metadata,
 )
 from frigate.util.file import get_event_snapshot
 
@@ -622,6 +624,59 @@ def get_classification_dataset(name: str):
     )
 
 
+@router.get(
+    "/classification/attributes",
+    summary="Get custom classification attributes",
+    description="""Returns custom classification attributes for a given object type.
+    Only includes models with classification_type set to 'attribute'.
+    By default returns a flat sorted list of all attribute labels.
+    If group_by_model is true, returns attributes grouped by model name.""",
+)
+def get_custom_attributes(
+    request: Request, object_type: str = None, group_by_model: bool = False
+):
+    models_with_attributes = {}
+
+    for (
+        model_key,
+        model_config,
+    ) in request.app.frigate_config.classification.custom.items():
+        if (
+            not model_config.enabled
+            or not model_config.object_config
+            or model_config.object_config.classification_type
+            != ObjectClassificationType.attribute
+        ):
+            continue
+
+        model_objects = getattr(model_config.object_config, "objects", []) or []
+        if object_type is not None and object_type not in model_objects:
+            continue
+
+        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
+        if not os.path.exists(dataset_dir):
+            continue
+
+        attributes = []
+        for category_name in os.listdir(dataset_dir):
+            category_dir = os.path.join(dataset_dir, category_name)
+            if os.path.isdir(category_dir) and category_name != "none":
+                attributes.append(category_name)
+
+        if attributes:
+            model_name = model_config.name or model_key
+            models_with_attributes[model_name] = sorted(attributes)
+
+    if group_by_model:
+        return JSONResponse(content=models_with_attributes)
+    else:
+        # Flatten to a unique sorted list
+        all_attributes = set()
+        for attributes in models_with_attributes.values():
+            all_attributes.update(attributes)
+        return JSONResponse(content=sorted(list(all_attributes)))
+
+
 @router.get(
     "/classification/{name}/train",
     summary="Get classification train images",
@@ -788,6 +843,12 @@ def rename_classification_category(
 
     try:
         os.rename(old_folder, new_folder)
+
+        # Mark dataset as ready to train by resetting training metadata
+        # This ensures the dataset is marked as changed after renaming
+        sanitized_name = sanitize_filename(name)
+        write_training_metadata(sanitized_name, 0)
+
         return JSONResponse(
             content=(
                 {

frigate/api/defs/query/events_query_parameters.py

@@ -12,6 +12,7 @@ class EventsQueryParams(BaseModel):
     labels: Optional[str] = "all"
     sub_label: Optional[str] = "all"
     sub_labels: Optional[str] = "all"
+    attributes: Optional[str] = "all"
     zone: Optional[str] = "all"
     zones: Optional[str] = "all"
     limit: Optional[int] = 100
@@ -58,6 +59,8 @@ class EventsSearchQueryParams(BaseModel):
     limit: Optional[int] = 50
     cameras: Optional[str] = "all"
     labels: Optional[str] = "all"
+    sub_labels: Optional[str] = "all"
+    attributes: Optional[str] = "all"
     zones: Optional[str] = "all"
     after: Optional[float] = None
     before: Optional[float] = None

frigate/api/defs/request/events_body.py

@@ -24,6 +24,13 @@ class EventsLPRBody(BaseModel):
     )
 
 
+class EventsAttributesBody(BaseModel):
+    attributes: List[str] = Field(
+        title="Selected classification attributes for the event",
+        default_factory=list,
+    )
+
+
 class EventsDescriptionBody(BaseModel):
     description: Union[str, None] = Field(title="The description of the event")
 

frigate/api/defs/request/export_recordings_body.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Optional, Union
 
 from pydantic import BaseModel, Field
 from pydantic.json_schema import SkipJsonSchema
@@ -16,5 +16,5 @@ class ExportRecordingsBody(BaseModel):
     source: PlaybackSourceEnum = Field(
         default=PlaybackSourceEnum.recordings, title="Playback source"
     )
-    name: str = Field(title="Friendly name", default=None, max_length=256)
+    name: Optional[str] = Field(title="Friendly name", default=None, max_length=256)
     image_path: Union[str, SkipJsonSchema[None]] = None

frigate/api/event.py

@@ -37,6 +37,7 @@ from frigate.api.defs.query.regenerate_query_parameters import (
     RegenerateQueryParameters,
 )
 from frigate.api.defs.request.events_body import (
+    EventsAttributesBody,
     EventsCreateBody,
     EventsDeleteBody,
     EventsDescriptionBody,
@@ -55,6 +56,7 @@ from frigate.api.defs.response.event_response import (
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.comms.event_metadata_updater import EventMetadataTypeEnum
+from frigate.config.classification import ObjectClassificationType
 from frigate.const import CLIPS_DIR, TRIGGER_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event, ReviewSegment, Timeline, Trigger
@@ -99,6 +101,8 @@ def events(
     if sub_labels == "all" and sub_label != "all":
         sub_labels = sub_label
 
+    attributes = unquote(params.attributes)
+
     zone = params.zone
     zones = params.zones
 
@@ -187,6 +191,17 @@ def events(
         sub_label_clause = reduce(operator.or_, sub_label_clauses)
         clauses.append((sub_label_clause))
 
+    if attributes != "all":
+        # Custom classification results are stored as data[model_name] = result_value
+        filtered_attributes = attributes.split(",")
+        attribute_clauses = []
+
+        for attr in filtered_attributes:
+            attribute_clauses.append(Event.data.cast("text") % f'*:"{attr}"*')
+
+        attribute_clause = reduce(operator.or_, attribute_clauses)
+        clauses.append(attribute_clause)
+
     if recognized_license_plate != "all":
         filtered_recognized_license_plates = recognized_license_plate.split(",")
 
@@ -492,6 +507,8 @@ def events_search(
     # Filters
     cameras = params.cameras
     labels = params.labels
+    sub_labels = params.sub_labels
+    attributes = params.attributes
     zones = params.zones
     after = params.after
     before = params.before
@@ -566,6 +583,38 @@ def events_search(
     if labels != "all":
         event_filters.append((Event.label << labels.split(",")))
 
+    if sub_labels != "all":
+        # use matching so joined sub labels are included
+        # for example a sub label 'bob' would get events
+        # with sub labels 'bob' and 'bob, john'
+        sub_label_clauses = []
+        filtered_sub_labels = sub_labels.split(",")
+
+        if "None" in filtered_sub_labels:
+            filtered_sub_labels.remove("None")
+            sub_label_clauses.append((Event.sub_label.is_null()))
+
+        for label in filtered_sub_labels:
+            sub_label_clauses.append(
+                (Event.sub_label.cast("text") == label)
+            )  # include exact matches
+
+            # include this label when part of a list
+            sub_label_clauses.append((Event.sub_label.cast("text") % f"*{label},*"))
+            sub_label_clauses.append((Event.sub_label.cast("text") % f"*, {label}*"))
+
+        event_filters.append((reduce(operator.or_, sub_label_clauses)))
+
+    if attributes != "all":
+        # Custom classification results are stored as data[model_name] = result_value
+        filtered_attributes = attributes.split(",")
+        attribute_clauses = []
+
+        for attr in filtered_attributes:
+            attribute_clauses.append(Event.data.cast("text") % f'*:"{attr}"*')
+
+        event_filters.append(reduce(operator.or_, attribute_clauses))
+
     if zones != "all":
         zone_clauses = []
         filtered_zones = zones.split(",")
@@ -1351,6 +1400,107 @@ async def set_plate(
     )
 
 
+@router.post(
+    "/events/{event_id}/attributes",
+    response_model=GenericResponse,
+    dependencies=[Depends(require_role(["admin"]))],
+    summary="Set custom classification attributes",
+    description=(
+        "Sets an event's custom classification attributes for all attribute-type "
+        "models that apply to the event's object type."
+    ),
+)
+async def set_attributes(
+    request: Request,
+    event_id: str,
+    body: EventsAttributesBody,
+):
+    try:
+        event: Event = Event.get(Event.id == event_id)
+        await require_camera_access(event.camera, request=request)
+    except DoesNotExist:
+        return JSONResponse(
+            content=({"success": False, "message": f"Event {event_id} not found."}),
+            status_code=404,
+        )
+
+    object_type = event.label
+    selected_attributes = set(body.attributes or [])
+    applied_updates: list[dict[str, str | float | None]] = []
+
+    for (
+        model_key,
+        model_config,
+    ) in request.app.frigate_config.classification.custom.items():
+        # Only apply to enabled attribute classifiers that target this object type
+        if (
+            not model_config.enabled
+            or not model_config.object_config
+            or model_config.object_config.classification_type
+            != ObjectClassificationType.attribute
+            or object_type not in (model_config.object_config.objects or [])
+        ):
+            continue
+
+        # Get available labels from dataset directory
+        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
+        available_labels = set()
+
+        if os.path.exists(dataset_dir):
+            for category_name in os.listdir(dataset_dir):
+                category_dir = os.path.join(dataset_dir, category_name)
+                if os.path.isdir(category_dir):
+                    available_labels.add(category_name)
+
+        if not available_labels:
+            logger.warning(
+                "No dataset found for custom attribute model %s at %s",
+                model_key,
+                dataset_dir,
+            )
+            continue
+
+        # Find all selected attributes that apply to this model
+        model_name = model_config.name or model_key
+        matching_attrs = selected_attributes & available_labels
+
+        if matching_attrs:
+            # Publish updates for each selected attribute
+            for attr in matching_attrs:
+                request.app.event_metadata_updater.publish(
+                    (event_id, model_name, attr, 1.0),
+                    EventMetadataTypeEnum.attribute.value,
+                )
+                applied_updates.append(
+                    {"model": model_name, "label": attr, "score": 1.0}
+                )
+        else:
+            # Clear this model's attribute
+            request.app.event_metadata_updater.publish(
+                (event_id, model_name, None, None),
+                EventMetadataTypeEnum.attribute.value,
+            )
+            applied_updates.append({"model": model_name, "label": None, "score": None})
+
+    if len(applied_updates) == 0:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "No matching attributes found for this object type.",
+            },
+            status_code=400,
+        )
+
+    return JSONResponse(
+        content={
+            "success": True,
+            "message": f"Updated {len(applied_updates)} attribute(s)",
+            "applied": applied_updates,
+        },
+        status_code=200,
+    )
+
+
 @router.post(
     "/events/{event_id}/description",
     response_model=GenericResponse,

frigate/app.py

@@ -100,6 +100,10 @@ class FrigateApp:
             )
             if (
                 config.semantic_search.enabled
+                or any(
+                    c.objects.genai.enabled or c.review.genai.enabled
+                    for c in config.cameras.values()
+                )
                 or config.lpr.enabled
                 or config.face_recognition.enabled
                 or len(config.classification.custom) > 0

frigate/comms/mqtt.py

@@ -225,7 +225,8 @@ class MqttClient(Communicator):
             "birdseye_mode",
             "review_alerts",
             "review_detections",
-            "genai",
+            "object_descriptions",
+            "review_descriptions",
         ]
 
         for name in self.config.cameras.keys():

frigate/comms/webpush.py

@@ -388,7 +388,7 @@ class WebPushClient(Communicator):
             else:
                 title = base_title
 
-            message = payload["after"]["data"]["metadata"]["scene"]
+            message = payload["after"]["data"]["metadata"]["shortSummary"]
         else:
             zone_names = payload["after"]["data"]["zones"]
             formatted_zone_names = []

frigate/config/config.py

@@ -28,6 +28,7 @@ from frigate.util.builtin import (
     get_ffmpeg_arg_list,
 )
 from frigate.util.config import (
+    CURRENT_CONFIG_VERSION,
     StreamInfoRetriever,
     convert_area_to_pixels,
     find_config_file,
@@ -76,11 +77,12 @@ logger = logging.getLogger(__name__)
 
 yaml = YAML()
 
-DEFAULT_CONFIG = """
+DEFAULT_CONFIG = f"""
 mqtt:
   enabled: False
 
-cameras: {}  # No cameras defined, UI wizard should be used
+cameras: {{}}  # No cameras defined, UI wizard should be used
+version: {CURRENT_CONFIG_VERSION}
 """
 
 DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
@@ -753,8 +755,7 @@ class FrigateConfig(FrigateBaseModel):
             if new_config and f.tell() == 0:
                 f.write(DEFAULT_CONFIG)
                 logger.info(
-                    "Created default config file, see the getting started docs \
-                    for configuration https://docs.frigate.video/guides/getting_started"
+                    "Created default config file, see the getting started docs for configuration: https://docs.frigate.video/guides/getting_started"
                 )
 
             f.seek(0)

frigate/const.py

@@ -77,6 +77,9 @@ FFMPEG_HWACCEL_RKMPP = "preset-rkmpp"
 FFMPEG_HWACCEL_AMF = "preset-amd-amf"
 FFMPEG_HVC1_ARGS = ["-tag:v", "hvc1"]
 
+# RKNN constants
+SUPPORTED_RK_SOCS = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
+
 # Regex constants
 
 REGEX_CAMERA_NAME = r"^[a-zA-Z0-9_-]+$"

frigate/data_processing/common/license_plate/mixin.py

@@ -374,6 +374,9 @@ class LicensePlateProcessingMixin:
                             combined_plate = re.sub(
                                 pattern, replacement, combined_plate
                             )
+                            logger.debug(
+                                f"{camera}: Processing replace rule: '{pattern}' -> '{replacement}', result: '{combined_plate}'"
+                            )
                     except re.error as e:
                         logger.warning(
                             f"{camera}: Invalid regex in replace_rules '{pattern}': {e}"
@@ -381,7 +384,7 @@ class LicensePlateProcessingMixin:
 
             if combined_plate != original_combined:
                 logger.debug(
-                    f"{camera}: Rules applied: '{original_combined}' -> '{combined_plate}'"
+                    f"{camera}: All rules applied: '{original_combined}' -> '{combined_plate}'"
                 )
 
             # Compute the combined area for qualifying boxes

frigate/data_processing/post/audio_transcription.py

@@ -131,8 +131,9 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
                 },
             )
 
-            # Embed the description
-            self.embeddings.embed_description(event_id, transcription)
+            # Embed the description if semantic search is enabled
+            if self.config.semantic_search.enabled:
+                self.embeddings.embed_description(event_id, transcription)
 
         except DoesNotExist:
             logger.debug("No recording found for audio transcription post-processing")

frigate/data_processing/post/object_descriptions.py

@@ -86,7 +86,11 @@ class ObjectDescriptionProcessor(PostProcessorApi):
                 and data["id"] not in self.early_request_sent
             ):
                 if data["has_clip"] and data["has_snapshot"]:
-                    event: Event = Event.get(Event.id == data["id"])
+                    try:
+                        event: Event = Event.get(Event.id == data["id"])
+                    except DoesNotExist:
+                        logger.error(f"Event {data['id']} not found")
+                        return
 
                     if (
                         not camera_config.objects.genai.objects
@@ -131,6 +135,8 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             )
         ):
             self._process_genai_description(event, camera_config, thumbnail)
+        else:
+            self.cleanup_event(event.id)
 
     def __regenerate_description(self, event_id: str, source: str, force: bool) -> None:
         """Regenerate the description for an event."""
@@ -204,6 +210,17 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             )
         return None
 
+    def cleanup_event(self, event_id: str) -> None:
+        """Clean up tracked event data to prevent memory leaks.
+
+        This should be called when an event ends, regardless of whether
+        genai processing is triggered.
+        """
+        if event_id in self.tracked_events:
+            del self.tracked_events[event_id]
+        if event_id in self.early_request_sent:
+            del self.early_request_sent[event_id]
+
     def _read_and_crop_snapshot(self, event: Event) -> bytes | None:
         """Read, decode, and crop the snapshot image."""
 
@@ -299,9 +316,8 @@ class ObjectDescriptionProcessor(PostProcessorApi):
             ),
         ).start()
 
-        # Delete tracked events based on the event_id
-        if event.id in self.tracked_events:
-            del self.tracked_events[event.id]
+        # Clean up tracked events and early request state
+        self.cleanup_event(event.id)
 
     def _genai_embed_description(self, event: Event, thumbnails: list[bytes]) -> None:
         """Embed the description for an event."""

frigate/data_processing/post/review_descriptions.py

@@ -92,7 +92,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
         pixels_per_image = width * height
         tokens_per_image = pixels_per_image / 1250
-        prompt_tokens = 3500
+        prompt_tokens = 3800
         response_tokens = 300
         available_tokens = context_size - prompt_tokens - response_tokens
         max_frames = int(available_tokens / tokens_per_image)
@@ -311,6 +311,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 start_ts,
                 end_ts,
                 events_with_context,
+                self.config.review.genai.preferred_language,
                 self.config.review.genai.debug_save_thumbnails,
             )
         else:

frigate/data_processing/post/types.py

@@ -8,6 +8,9 @@ class ReviewMetadata(BaseModel):
     scene: str = Field(
         description="A comprehensive description of the setting and entities, including relevant context and plausible inferences if supported by visual evidence."
     )
+    shortSummary: str = Field(
+        description="A brief 2-sentence summary of the scene, suitable for notifications. Should capture the key activity and context without full detail."
+    )
     confidence: float = Field(
         description="A float between 0 and 1 representing your overall confidence in this analysis."
     )

frigate/data_processing/real_time/bird.py

@@ -13,7 +13,7 @@ from frigate.comms.event_metadata_updater import (
 )
 from frigate.config import FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.util.object import calculate_region
 
 from ..types import DataProcessorMetrics
@@ -80,13 +80,14 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
         except Exception as e:
             logger.error(f"Failed to download {path}: {e}")
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
-        self.interpreter = Interpreter(
-            model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
-            num_threads=2,
-        )
-        self.interpreter.allocate_tensors()
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
+            self.interpreter = Interpreter(
+                model_path=os.path.join(MODEL_CACHE_DIR, "bird/bird.tflite"),
+                num_threads=2,
+            )
+            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
 

frigate/data_processing/real_time/custom_classification.py

@@ -21,7 +21,7 @@ from frigate.config.classification import (
     ObjectClassificationType,
 )
 from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
 from frigate.util.object import box_overlaps, calculate_region
@@ -52,7 +52,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.requestor = requestor
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Interpreter | None = None
+        self.interpreter: Interpreter = None
         self.tensor_input_details: dict[str, Any] | None = None
         self.tensor_output_details: dict[str, Any] | None = None
         self.labelmap: dict[int, str] = {}
@@ -72,8 +72,12 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.last_run = datetime.datetime.now().timestamp()
         self.__build_detector()
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
+        try:
+            from tflite_runtime.interpreter import Interpreter
+        except ModuleNotFoundError:
+            from tensorflow.lite.python.interpreter import Interpreter
+
         model_path = os.path.join(self.model_dir, "model.tflite")
         labelmap_path = os.path.join(self.model_dir, "labelmap.txt")
 
@@ -84,11 +88,13 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        self.interpreter = Interpreter(
-            model_path=model_path,
-            num_threads=2,
-        )
-        self.interpreter.allocate_tensors()
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
+            self.interpreter = Interpreter(
+                model_path=model_path,
+                num_threads=2,
+            )
+            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0)
@@ -224,28 +230,34 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         if not should_run:
             return
 
-        x, y, x2, y2 = calculate_region(
-            frame.shape,
-            crop[0],
-            crop[1],
-            crop[2],
-            crop[3],
-            224,
-            1.0,
-        )
-
         rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
-        frame = rgb[
-            y:y2,
-            x:x2,
-        ]
+        height, width = rgb.shape[:2]
 
-        if frame.shape != (224, 224):
-            try:
-                resized_frame = cv2.resize(frame, (224, 224))
-            except Exception:
-                logger.warning("Failed to resize image for state classification")
-                return
+        # Convert normalized crop coordinates to pixel values
+        x1 = int(camera_config.crop[0] * width)
+        y1 = int(camera_config.crop[1] * height)
+        x2 = int(camera_config.crop[2] * width)
+        y2 = int(camera_config.crop[3] * height)
+
+        # Clip coordinates to frame boundaries
+        x1 = max(0, min(x1, width))
+        y1 = max(0, min(y1, height))
+        x2 = max(0, min(x2, width))
+        y2 = max(0, min(y2, height))
+
+        if x2 <= x1 or y2 <= y1:
+            logger.warning(
+                f"Invalid crop coordinates for {camera}: [{x1}, {y1}, {x2}, {y2}]"
+            )
+            return
+
+        frame = rgb[y1:y2, x1:x2]
+
+        try:
+            resized_frame = cv2.resize(frame, (224, 224))
+        except Exception:
+            logger.warning("Failed to resize image for state classification")
+            return
 
         if self.interpreter is None:
             # When interpreter is None, always save (score is 0.0, which is < 1.0)
@@ -345,7 +357,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         self.model_config = model_config
         self.model_dir = os.path.join(MODEL_CACHE_DIR, self.model_config.name)
         self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
-        self.interpreter: Interpreter | None = None
+        self.interpreter: Interpreter = None
         self.sub_label_publisher = sub_label_publisher
         self.requestor = requestor
         self.tensor_input_details: dict[str, Any] | None = None
@@ -366,7 +378,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
 
         self.__build_detector()
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __build_detector(self) -> None:
         model_path = os.path.join(self.model_dir, "model.tflite")
         labelmap_path = os.path.join(self.model_dir, "labelmap.txt")
@@ -378,11 +389,13 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             self.labelmap = {}
             return
 
-        self.interpreter = Interpreter(
-            model_path=model_path,
-            num_threads=2,
-        )
-        self.interpreter.allocate_tensors()
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
+            self.interpreter = Interpreter(
+                model_path=model_path,
+                num_threads=2,
+            )
+            self.interpreter.allocate_tensors()
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0)
@@ -508,6 +521,13 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
                 0.0,
                 max_files=save_attempts,
             )
+
+            # Still track history even when model doesn't exist to respect MAX_OBJECT_CLASSIFICATIONS
+            # Add an entry with "unknown" label so the history limit is enforced
+            if object_id not in self.classification_history:
+                self.classification_history[object_id] = []
+
+            self.classification_history[object_id].append(("unknown", 0.0, now))
             return
 
         input = np.expand_dims(resized_crop, axis=0)
@@ -649,5 +669,5 @@ def write_classification_attempt(
 
         if len(files) > max_files:
             os.unlink(os.path.join(folder, files[-1]))
-    except FileNotFoundError:
+    except (FileNotFoundError, OSError):
         pass

frigate/detectors/detection_runners.py

@@ -131,6 +131,7 @@ class ONNXModelRunner(BaseModelRunner):
 
         return model_type in [
             EnrichmentModelTypeEnum.paddleocr.value,
+            EnrichmentModelTypeEnum.yolov9_license_plate.value,
             EnrichmentModelTypeEnum.jina_v1.value,
             EnrichmentModelTypeEnum.jina_v2.value,
             EnrichmentModelTypeEnum.facenet.value,
@@ -138,8 +139,31 @@ class ONNXModelRunner(BaseModelRunner):
             ModelTypeEnum.dfine.value,
         ]
 
-    def __init__(self, ort: ort.InferenceSession):
+    @staticmethod
+    def is_concurrent_model(model_type: str | None) -> bool:
+        """Check if model requires thread locking for concurrent inference.
+
+        Some models (like JinaV2) share one runner between text and vision embeddings
+        called from different threads, requiring thread synchronization.
+        """
+        if not model_type:
+            return False
+
+        # Import here to avoid circular imports
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+        return model_type == EnrichmentModelTypeEnum.jina_v2.value
+
+    def __init__(self, ort: ort.InferenceSession, model_type: str | None = None):
         self.ort = ort
+        self.model_type = model_type
+
+        # Thread lock to prevent concurrent inference (needed for JinaV2 which shares
+        # one runner between text and vision embeddings called from different threads)
+        if self.is_concurrent_model(model_type):
+            self._inference_lock = threading.Lock()
+        else:
+            self._inference_lock = None
 
     def get_input_names(self) -> list[str]:
         return [input.name for input in self.ort.get_inputs()]
@@ -149,6 +173,10 @@ class ONNXModelRunner(BaseModelRunner):
         return self.ort.get_inputs()[0].shape[3]
 
     def run(self, input: dict[str, Any]) -> Any | None:
+        if self._inference_lock:
+            with self._inference_lock:
+                return self.ort.run(None, input)
+
         return self.ort.run(None, input)
 
 
@@ -169,6 +197,7 @@ class CudaGraphRunner(BaseModelRunner):
 
         return model_type not in [
             ModelTypeEnum.yolonas.value,
+            ModelTypeEnum.dfine.value,
             EnrichmentModelTypeEnum.paddleocr.value,
             EnrichmentModelTypeEnum.jina_v1.value,
             EnrichmentModelTypeEnum.jina_v2.value,
@@ -574,5 +603,6 @@ def get_optimized_runner(
             ),
             providers=providers,
             provider_options=options,
-        )
+        ),
+        model_type=model_type,
     )

frigate/detectors/plugins/cpu_tfl.py

@@ -5,7 +5,7 @@ from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 
 from ..detector_utils import tflite_detect_raw, tflite_init
 
@@ -28,12 +28,13 @@ class CpuDetectorConfig(BaseDetectorConfig):
 class CpuTfl(DetectionApi):
     type_key = DETECTOR_KEY
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __init__(self, detector_config: CpuDetectorConfig):
-        interpreter = Interpreter(
-            model_path=detector_config.model.path,
-            num_threads=detector_config.num_threads or 3,
-        )
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
+            interpreter = Interpreter(
+                model_path=detector_config.model.path,
+                num_threads=detector_config.num_threads or 3,
+            )
 
         tflite_init(self, interpreter)
 

frigate/detectors/plugins/rknn.py

@@ -8,7 +8,7 @@ import cv2
 import numpy as np
 from pydantic import Field
 
-from frigate.const import MODEL_CACHE_DIR
+from frigate.const import MODEL_CACHE_DIR, SUPPORTED_RK_SOCS
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import RKNNModelRunner
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
@@ -19,8 +19,6 @@ logger = logging.getLogger(__name__)
 
 DETECTOR_KEY = "rknn"
 
-supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
-
 supported_models = {
     ModelTypeEnum.yologeneric: "^frigate-fp16-yolov9-[cemst]$",
     ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$",
@@ -82,9 +80,9 @@ class Rknn(DetectionApi):
         except FileNotFoundError:
             raise Exception("Make sure to run docker in privileged mode.")
 
-        if soc not in supported_socs:
+        if soc not in SUPPORTED_RK_SOCS:
             raise Exception(
-                f"Your SoC is not supported. Your SoC is: {soc}. Currently these SoCs are supported: {supported_socs}."
+                f"Your SoC is not supported. Your SoC is: {soc}. Currently these SoCs are supported: {SUPPORTED_RK_SOCS}."
             )
 
         return soc

frigate/embeddings/maintainer.py

@@ -203,7 +203,9 @@ class EmbeddingMaintainer(threading.Thread):
         # post processors
         self.post_processors: list[PostProcessorApi] = []
 
-        if any(c.review.genai.enabled_in_config for c in self.config.cameras.values()):
+        if self.genai_client is not None and any(
+            c.review.genai.enabled_in_config for c in self.config.cameras.values()
+        ):
             self.post_processors.append(
                 ReviewDescriptionProcessor(
                     self.config, self.requestor, self.metrics, self.genai_client
@@ -244,7 +246,9 @@ class EmbeddingMaintainer(threading.Thread):
             )
             self.post_processors.append(semantic_trigger_processor)
 
-        if any(c.objects.genai.enabled_in_config for c in self.config.cameras.values()):
+        if self.genai_client is not None and any(
+            c.objects.genai.enabled_in_config for c in self.config.cameras.values()
+        ):
             self.post_processors.append(
                 ObjectDescriptionProcessor(
                     self.config,
@@ -522,6 +526,8 @@ class EmbeddingMaintainer(threading.Thread):
                     )
                 elif isinstance(processor, ObjectDescriptionProcessor):
                     if not updated_db:
+                        # Still need to cleanup tracked events even if not processing
+                        processor.cleanup_event(event_id)
                         continue
 
                     processor.process_data(
@@ -627,7 +633,7 @@ class EmbeddingMaintainer(threading.Thread):
 
         camera, frame_name, _, _, motion_boxes, _ = data
 
-        if not camera or len(motion_boxes) == 0:
+        if not camera or len(motion_boxes) == 0 or camera not in self.config.cameras:
             return
 
         camera_config = self.config.cameras[camera]

frigate/embeddings/onnx/face_embedding.py

@@ -8,7 +8,7 @@ import numpy as np
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors.detection_runners import get_optimized_runner
 from frigate.embeddings.types import EnrichmentModelTypeEnum
-from frigate.log import redirect_output_to_logger
+from frigate.log import suppress_stderr_during
 from frigate.util.downloader import ModelDownloader
 
 from ...config import FaceRecognitionConfig
@@ -57,17 +57,18 @@ class FaceNetEmbedding(BaseEmbedding):
             self._load_model_and_utils()
             logger.debug(f"models are already downloaded for {self.model_name}")
 
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def _load_model_and_utils(self):
         if self.runner is None:
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = Interpreter(
-                model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
-                num_threads=2,
-            )
-            self.runner.allocate_tensors()
+            # Suppress TFLite delegate creation messages that bypass Python logging
+            with suppress_stderr_during("tflite_interpreter_init"):
+                self.runner = Interpreter(
+                    model_path=os.path.join(MODEL_CACHE_DIR, "facedet/facenet.tflite"),
+                    num_threads=2,
+                )
+                self.runner.allocate_tensors()
             self.tensor_input_details = self.runner.get_input_details()
             self.tensor_output_details = self.runner.get_output_details()
 

frigate/embeddings/onnx/jina_v1_embedding.py

@@ -186,6 +186,9 @@ class JinaV1ImageEmbedding(BaseEmbedding):
                 download_func=self._download_model,
             )
             self.downloader.ensure_model_files()
+            # Avoid lazy loading in worker threads: block until downloads complete
+            # and load the model on the main thread during initialization.
+            self._load_model_and_utils()
         else:
             self.downloader = None
             ModelDownloader.mark_files_state(

frigate/embeddings/onnx/jina_v2_embedding.py

@@ -3,6 +3,7 @@
 import io
 import logging
 import os
+import threading
 
 import numpy as np
 from PIL import Image
@@ -53,6 +54,11 @@ class JinaV2Embedding(BaseEmbedding):
         self.tokenizer = None
         self.image_processor = None
         self.runner = None
+
+        # Lock to prevent concurrent calls (text and vision share this instance)
+        self._call_lock = threading.Lock()
+
+        # download the model and tokenizer
         files_names = list(self.download_urls.keys()) + [self.tokenizer_file]
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -65,6 +71,9 @@ class JinaV2Embedding(BaseEmbedding):
                 download_func=self._download_model,
             )
             self.downloader.ensure_model_files()
+            # Avoid lazy loading in worker threads: block until downloads complete
+            # and load the model on the main thread during initialization.
+            self._load_model_and_utils()
         else:
             self.downloader = None
             ModelDownloader.mark_files_state(
@@ -197,37 +206,40 @@ class JinaV2Embedding(BaseEmbedding):
     def __call__(
         self, inputs: list[str] | list[Image.Image] | list[str], embedding_type=None
     ) -> list[np.ndarray]:
-        self.embedding_type = embedding_type
-        if not self.embedding_type:
-            raise ValueError(
-                "embedding_type must be specified either in __init__ or __call__"
-            )
+        # Lock the entire call to prevent race conditions when text and vision
+        # embeddings are called concurrently from different threads
+        with self._call_lock:
+            self.embedding_type = embedding_type
+            if not self.embedding_type:
+                raise ValueError(
+                    "embedding_type must be specified either in __init__ or __call__"
+                )
 
-        self._load_model_and_utils()
-        processed = self._preprocess_inputs(inputs)
-        batch_size = len(processed)
+            self._load_model_and_utils()
+            processed = self._preprocess_inputs(inputs)
+            batch_size = len(processed)
 
-        # Prepare ONNX inputs with matching batch sizes
-        onnx_inputs = {}
-        if self.embedding_type == "text":
-            onnx_inputs["input_ids"] = np.stack([x[0] for x in processed])
-            onnx_inputs["pixel_values"] = np.zeros(
-                (batch_size, 3, 512, 512), dtype=np.float32
-            )
-        elif self.embedding_type == "vision":
-            onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64)
-            onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed])
-        else:
-            raise ValueError("Invalid embedding type")
+            # Prepare ONNX inputs with matching batch sizes
+            onnx_inputs = {}
+            if self.embedding_type == "text":
+                onnx_inputs["input_ids"] = np.stack([x[0] for x in processed])
+                onnx_inputs["pixel_values"] = np.zeros(
+                    (batch_size, 3, 512, 512), dtype=np.float32
+                )
+            elif self.embedding_type == "vision":
+                onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64)
+                onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed])
+            else:
+                raise ValueError("Invalid embedding type")
 
-        # Run inference
-        outputs = self.runner.run(onnx_inputs)
-        if self.embedding_type == "text":
-            embeddings = outputs[2]  # text embeddings
-        elif self.embedding_type == "vision":
-            embeddings = outputs[3]  # image embeddings
-        else:
-            raise ValueError("Invalid embedding type")
+            # Run inference
+            outputs = self.runner.run(onnx_inputs)
+            if self.embedding_type == "text":
+                embeddings = outputs[2]  # text embeddings
+            elif self.embedding_type == "vision":
+                embeddings = outputs[3]  # image embeddings
+            else:
+                raise ValueError("Invalid embedding type")
 
-        embeddings = self._postprocess_outputs(embeddings)
-        return [embedding for embedding in embeddings]
+            embeddings = self._postprocess_outputs(embeddings)
+            return [embedding for embedding in embeddings]

frigate/events/audio.py

@@ -34,7 +34,7 @@ from frigate.data_processing.real_time.audio_transcription import (
     AudioTranscriptionRealTimeProcessor,
 )
 from frigate.ffmpeg_presets import parse_preset_input
-from frigate.log import LogPipe, redirect_output_to_logger
+from frigate.log import LogPipe, suppress_stderr_during
 from frigate.object_detection.base import load_labels
 from frigate.util.builtin import get_ffmpeg_arg_list
 from frigate.util.process import FrigateProcess
@@ -367,17 +367,17 @@ class AudioEventMaintainer(threading.Thread):
 
 
 class AudioTfl:
-    @redirect_output_to_logger(logger, logging.DEBUG)
     def __init__(self, stop_event: threading.Event, num_threads=2):
         self.stop_event = stop_event
         self.num_threads = num_threads
         self.labels = load_labels("/audio-labelmap.txt", prefill=521)
-        self.interpreter = Interpreter(
-            model_path="/cpu_audio_model.tflite",
-            num_threads=self.num_threads,
-        )
-
-        self.interpreter.allocate_tensors()
+        # Suppress TFLite delegate creation messages that bypass Python logging
+        with suppress_stderr_during("tflite_interpreter_init"):
+            self.interpreter = Interpreter(
+                model_path="/cpu_audio_model.tflite",
+                num_threads=self.num_threads,
+            )
+            self.interpreter.allocate_tensors()
 
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()

frigate/events/maintainer.py

@@ -46,7 +46,7 @@ def should_update_state(prev_event: Event, current_event: Event) -> bool:
     if prev_event["sub_label"] != current_event["sub_label"]:
         return True
 
-    if len(prev_event["current_zones"]) < len(current_event["current_zones"]):
+    if set(prev_event["current_zones"]) != set(current_event["current_zones"]):
         return True
 
     return False

frigate/ffmpeg_presets.py

@@ -153,7 +153,7 @@ PRESETS_HW_ACCEL_ENCODE_BIRDSEYE = {
     FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device {3} {1} -c:v h264_vaapi -g 50 -bf 0 -profile:v high -level:v 4.1 -sei:v 0 -an -vf format=vaapi|nv12,hwupload {2}",
     "preset-intel-qsv-h264": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v high -level:v 4.1 -async_depth:v 1 {2}",
     "preset-intel-qsv-h265": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v main -level:v 4.1 -async_depth:v 1 {2}",
-    FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} -hwaccel device {3} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
+    FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
     "preset-jetson-h264": "{0} -hide_banner {1} -c:v h264_nvmpi -profile high {2}",
     "preset-jetson-h265": "{0} -hide_banner {1} -c:v h264_nvmpi -profile main {2}",
     FFMPEG_HWACCEL_RKMPP: "{0} -hide_banner {1} -c:v h264_rkmpp -profile:v high {2}",

frigate/genai/__init__.py

@@ -101,6 +101,7 @@ When forming your description:
 Your response MUST be a flat JSON object with:
 - `title` (string): A concise, direct title that describes the primary action or event in the sequence, not just what you literally see. Use spatial context when available to make titles more meaningful. When multiple objects/actions are present, prioritize whichever is most prominent or occurs first. Use names from "Objects in Scene" based on what you visually observe. If you see both a name and an unidentified object of the same type but visually observe only one person/object, use ONLY the name. Examples: "Joe walking dog", "Person taking out trash", "Vehicle arriving in driveway", "Joe accessing vehicle", "Person leaving porch for driveway".
 - `scene` (string): A narrative description of what happens across the sequence from start to finish, in chronological order. Start by describing how the sequence begins, then describe the progression of events. **Describe all significant movements and actions in the order they occur.** For example, if a vehicle arrives and then a person exits, describe both actions sequentially. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign.
+- `shortSummary` (string): A brief 2-sentence summary of the scene, suitable for notifications. Should capture the key activity and context without full detail. This should be a condensed version of the scene description above.
 - `confidence` (float): 0-1 confidence in your analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous. Lower confidence when the sequence is unclear, objects are partially obscured, or context is ambiguous.
 - `potential_threat_level` (integer): 0, 1, or 2 as defined in "Normal Activity Patterns for This Property" above. Your threat level must be consistent with your scene description and the guidance above.
 {get_concern_prompt()}
@@ -178,6 +179,7 @@ Each line represents a detection state, not necessarily unique individuals. Pare
         start_ts: float,
         end_ts: float,
         events: list[dict[str, Any]],
+        preferred_language: str | None,
         debug_save: bool,
     ) -> str | None:
         """Generate a summary of review item descriptions over a period of time."""
@@ -191,6 +193,8 @@ Input format: Each event is a JSON object with:
 - "title", "scene", "confidence", "potential_threat_level" (0-2), "other_concerns", "camera", "time", "start_time", "end_time"
 - "context": array of related events from other cameras that occurred during overlapping time periods
 
+**Note: Use the "scene" field for event descriptions in the report. Ignore any "shortSummary" field if present.**
+
 Report Structure - Use this EXACT format:
 
 # Security Summary - {time_range}
@@ -232,6 +236,9 @@ Guidelines:
         for event in events:
             timeline_summary_prompt += f"\n{event}\n"
 
+        if preferred_language:
+            timeline_summary_prompt += f"\nProvide your answer in {preferred_language}"
+
         if debug_save:
             with open(
                 os.path.join(

frigate/genai/ollama.py

@@ -3,7 +3,7 @@
 import logging
 from typing import Any, Optional
 
-from httpx import TimeoutException
+from httpx import RemoteProtocolError, TimeoutException
 from ollama import Client as ApiClient
 from ollama import ResponseError
 
@@ -68,7 +68,12 @@ class OllamaClient(GenAIClient):
                 f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"
             )
             return result["response"].strip()
-        except (TimeoutException, ResponseError, ConnectionError) as e:
+        except (
+            TimeoutException,
+            ResponseError,
+            RemoteProtocolError,
+            ConnectionError,
+        ) as e:
             logger.warning("Ollama returned an error: %s", str(e))
             return None
 

frigate/log.py

@@ -80,10 +80,15 @@ def apply_log_levels(default: str, log_levels: dict[str, LogLevel]) -> None:
     log_levels = {
         "absl": LogLevel.error,
         "httpx": LogLevel.error,
+        "h5py": LogLevel.error,
+        "keras": LogLevel.error,
         "matplotlib": LogLevel.error,
         "tensorflow": LogLevel.error,
+        "tensorflow.python": LogLevel.error,
         "werkzeug": LogLevel.error,
         "ws4py": LogLevel.error,
+        "PIL": LogLevel.warning,
+        "numba": LogLevel.warning,
         **log_levels,
     }
 
@@ -318,3 +323,31 @@ def suppress_os_output(func: Callable) -> Callable:
         return result
 
     return wrapper
+
+
+@contextmanager
+def suppress_stderr_during(operation_name: str) -> Generator[None, None, None]:
+    """
+    Context manager to suppress stderr output during a specific operation.
+
+    Useful for silencing LLVM debug output, CUDA messages, and other native
+    library logging that cannot be controlled via Python logging or environment
+    variables. Completely redirects file descriptor 2 (stderr) to /dev/null.
+
+    Usage:
+        with suppress_stderr_during("model_conversion"):
+            converter = tf.lite.TFLiteConverter.from_keras_model(model)
+            tflite_model = converter.convert()
+
+    Args:
+        operation_name: Name of the operation for debugging purposes
+    """
+    original_stderr_fd = os.dup(2)
+    devnull = os.open(os.devnull, os.O_WRONLY)
+    try:
+        os.dup2(devnull, 2)
+        yield
+    finally:
+        os.dup2(original_stderr_fd, 2)
+        os.close(devnull)
+        os.close(original_stderr_fd)

frigate/output/output.py

@@ -139,9 +139,11 @@ class OutputProcess(FrigateProcess):
             if CameraConfigUpdateEnum.add in updates:
                 for camera in updates["add"]:
                     jsmpeg_cameras[camera] = JsmpegCamera(
-                        cam_config, self.stop_event, websocket_server
+                        self.config.cameras[camera], self.stop_event, websocket_server
+                    )
+                    preview_recorders[camera] = PreviewRecorder(
+                        self.config.cameras[camera]
                     )
-                    preview_recorders[camera] = PreviewRecorder(cam_config)
                     preview_write_times[camera] = 0
 
                     if (

frigate/record/cleanup.py

@@ -119,6 +119,7 @@ class RecordingCleanup(threading.Thread):
                 Recordings.path,
                 Recordings.objects,
                 Recordings.motion,
+                Recordings.dBFS,
             )
             .where(
                 (Recordings.camera == config.name)
@@ -126,6 +127,7 @@ class RecordingCleanup(threading.Thread):
                     (
                         (Recordings.end_time < continuous_expire_date)
                         & (Recordings.motion == 0)
+                        & (Recordings.dBFS == 0)
                     )
                     | (Recordings.end_time < motion_expire_date)
                 )
@@ -185,6 +187,7 @@ class RecordingCleanup(threading.Thread):
                     mode == RetainModeEnum.motion
                     and recording.motion == 0
                     and recording.objects == 0
+                    and recording.dBFS == 0
                 )
                 or (mode == RetainModeEnum.active_objects and recording.objects == 0)
             ):

frigate/record/maintainer.py

@@ -67,7 +67,7 @@ class SegmentInfo:
         if (
             not keep
             and retain_mode == RetainModeEnum.motion
-            and (self.motion_count > 0 or self.average_dBFS > 0)
+            and (self.motion_count > 0 or self.average_dBFS != 0)
         ):
             keep = True
 

frigate/stats/util.py

@@ -42,11 +42,10 @@ def get_latest_version(config: FrigateConfig) -> str:
             "https://api.github.com/repos/blakeblackshear/frigate/releases/latest",
             timeout=10,
         )
+        response = request.json()
     except (RequestException, JSONDecodeError):
         return "unknown"
 
-    response = request.json()
-
     if request.ok and response and "tag_name" in response:
         return str(response.get("tag_name").replace("v", ""))
     else:

frigate/timeline.py

@@ -86,11 +86,11 @@ class TimelineProcessor(threading.Thread):
         event_data: dict[Any, Any],
     ) -> bool:
         """Handle object detection."""
-        save = False
         camera_config = self.config.cameras[camera]
         event_id = event_data["id"]
 
-        timeline_entry = {
+        # Base timeline entry data that all entries will share
+        base_entry = {
             Timeline.timestamp: event_data["frame_time"],
             Timeline.camera: camera,
             Timeline.source: "tracked_object",
@@ -123,40 +123,64 @@ class TimelineProcessor(threading.Thread):
                 e[Timeline.data]["sub_label"] = event_data["sub_label"]
 
         if event_type == EventStateEnum.start:
+            timeline_entry = base_entry.copy()
             timeline_entry[Timeline.class_type] = "visible"
-            save = True
+            self.insert_or_save(timeline_entry, prev_event_data, event_data)
         elif event_type == EventStateEnum.update:
+            # Check all conditions and create timeline entries for each change
+            entries_to_save = []
+
+            # Check for zone changes
+            prev_zones = set(prev_event_data["current_zones"])
+            current_zones = set(event_data["current_zones"])
+            zones_changed = prev_zones != current_zones
+
+            # Only save "entered_zone" events when the object is actually IN zones
             if (
-                len(prev_event_data["current_zones"]) < len(event_data["current_zones"])
+                zones_changed
                 and not event_data["stationary"]
+                and len(current_zones) > 0
             ):
-                timeline_entry[Timeline.class_type] = "entered_zone"
-                timeline_entry[Timeline.data]["zones"] = event_data["current_zones"]
-                save = True
-            elif prev_event_data["stationary"] != event_data["stationary"]:
-                timeline_entry[Timeline.class_type] = (
+                zone_entry = base_entry.copy()
+                zone_entry[Timeline.class_type] = "entered_zone"
+                zone_entry[Timeline.data] = base_entry[Timeline.data].copy()
+                zone_entry[Timeline.data]["zones"] = event_data["current_zones"]
+                entries_to_save.append(zone_entry)
+
+            # Check for stationary status change
+            if prev_event_data["stationary"] != event_data["stationary"]:
+                stationary_entry = base_entry.copy()
+                stationary_entry[Timeline.class_type] = (
                     "stationary" if event_data["stationary"] else "active"
                 )
-                save = True
-            elif prev_event_data["attributes"] == {} and event_data["attributes"] != {}:
-                timeline_entry[Timeline.class_type] = "attribute"
-                timeline_entry[Timeline.data]["attribute"] = list(
+                stationary_entry[Timeline.data] = base_entry[Timeline.data].copy()
+                entries_to_save.append(stationary_entry)
+
+            # Check for new attributes
+            if prev_event_data["attributes"] == {} and event_data["attributes"] != {}:
+                attribute_entry = base_entry.copy()
+                attribute_entry[Timeline.class_type] = "attribute"
+                attribute_entry[Timeline.data] = base_entry[Timeline.data].copy()
+                attribute_entry[Timeline.data]["attribute"] = list(
                     event_data["attributes"].keys()
                 )[0]
 
                 if len(event_data["current_attributes"]) > 0:
-                    timeline_entry[Timeline.data]["attribute_box"] = to_relative_box(
+                    attribute_entry[Timeline.data]["attribute_box"] = to_relative_box(
                         camera_config.detect.width,
                         camera_config.detect.height,
                         event_data["current_attributes"][0]["box"],
                     )
 
-                save = True
-        elif event_type == EventStateEnum.end:
-            timeline_entry[Timeline.class_type] = "gone"
-            save = True
+                entries_to_save.append(attribute_entry)
 
-        if save:
+            # Save all entries
+            for entry in entries_to_save:
+                self.insert_or_save(entry, prev_event_data, event_data)
+
+        elif event_type == EventStateEnum.end:
+            timeline_entry = base_entry.copy()
+            timeline_entry[Timeline.class_type] = "gone"
             self.insert_or_save(timeline_entry, prev_event_data, event_data)
 
     def handle_api_entry(

frigate/util/classification.py

@@ -19,9 +19,10 @@ from frigate.const import (
     PROCESS_PRIORITY_LOW,
     UPDATE_MODEL_STATE,
 )
-from frigate.log import redirect_output_to_logger
+from frigate.log import redirect_output_to_logger, suppress_stderr_during
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.types import ModelStatusTypesEnum
+from frigate.util.downloader import ModelDownloader
 from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import get_image_from_recording
 from frigate.util.process import FrigateProcess
@@ -121,6 +122,10 @@ def get_dataset_image_count(model_name: str) -> int:
 
 class ClassificationTrainingProcess(FrigateProcess):
     def __init__(self, model_name: str) -> None:
+        self.BASE_WEIGHT_URL = os.environ.get(
+            "TF_KERAS_MOBILENET_V2_WEIGHTS_URL",
+            "",
+        )
         super().__init__(
             stop_event=None,
             priority=PROCESS_PRIORITY_LOW,
@@ -179,11 +184,23 @@ class ClassificationTrainingProcess(FrigateProcess):
                 )
                 return False
 
+            weights_path = "imagenet"
+            # Download MobileNetV2 weights if not present
+            if self.BASE_WEIGHT_URL:
+                weights_path = os.path.join(
+                    MODEL_CACHE_DIR, "MobileNet", "mobilenet_v2_weights.h5"
+                )
+                if not os.path.exists(weights_path):
+                    logger.info("Downloading MobileNet V2 weights file")
+                    ModelDownloader.download_from_url(
+                        self.BASE_WEIGHT_URL, weights_path
+                    )
+
             # Start with imagenet base model with 35% of channels in each layer
             base_model = MobileNetV2(
                 input_shape=(224, 224, 3),
                 include_top=False,
-                weights="imagenet",
+                weights=weights_path,
                 alpha=0.35,
             )
             base_model.trainable = False  # Freeze pre-trained layers
@@ -233,15 +250,20 @@ class ClassificationTrainingProcess(FrigateProcess):
             logger.debug(f"Converting {self.model_name} to TFLite...")
 
             # convert model to tflite
-            converter = tf.lite.TFLiteConverter.from_keras_model(model)
-            converter.optimizations = [tf.lite.Optimize.DEFAULT]
-            converter.representative_dataset = (
-                self.__generate_representative_dataset_factory(dataset_dir)
-            )
-            converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-            converter.inference_input_type = tf.uint8
-            converter.inference_output_type = tf.uint8
-            tflite_model = converter.convert()
+            # Suppress stderr during conversion to avoid LLVM debug output
+            # (fully_quantize, inference_type, MLIR optimization messages, etc)
+            with suppress_stderr_during("tflite_conversion"):
+                converter = tf.lite.TFLiteConverter.from_keras_model(model)
+                converter.optimizations = [tf.lite.Optimize.DEFAULT]
+                converter.representative_dataset = (
+                    self.__generate_representative_dataset_factory(dataset_dir)
+                )
+                converter.target_spec.supported_ops = [
+                    tf.lite.OpsSet.TFLITE_BUILTINS_INT8
+                ]
+                converter.inference_input_type = tf.uint8
+                converter.inference_output_type = tf.uint8
+                tflite_model = converter.convert()
 
             # write model
             model_path = os.path.join(model_dir, "model.tflite")
@@ -338,8 +360,6 @@ def collect_state_classification_examples(
         cameras: Dict mapping camera names to normalized crop coordinates [x1, y1, x2, y2] (0-1)
     """
     dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
-    temp_dir = os.path.join(dataset_dir, "temp")
-    os.makedirs(temp_dir, exist_ok=True)
 
     # Step 1: Get review items for the cameras
     camera_names = list(cameras.keys())
@@ -354,6 +374,10 @@ def collect_state_classification_examples(
         logger.warning(f"No review items found for cameras: {camera_names}")
         return
 
+    # The temp directory is only created when there are review_items.
+    temp_dir = os.path.join(dataset_dir, "temp")
+    os.makedirs(temp_dir, exist_ok=True)
+
     # Step 2: Create balanced timestamp selection (100 samples)
     timestamps = _select_balanced_timestamps(review_items, target_count=100)
 
@@ -482,6 +506,10 @@ def _extract_keyframes(
     """
     Extract keyframes from recordings at specified timestamps and crop to specified regions.
 
+    This implementation batches work by running multiple ffmpeg snapshot commands
+    concurrently, which significantly reduces total runtime compared to
+    processing each timestamp serially.
+
     Args:
         ffmpeg_path: Path to ffmpeg binary
         timestamps: List of timestamp dicts from _select_balanced_timestamps
@@ -491,15 +519,21 @@ def _extract_keyframes(
     Returns:
         List of paths to successfully extracted and cropped keyframe images
     """
-    keyframe_paths = []
+    from concurrent.futures import ThreadPoolExecutor, as_completed
 
-    for idx, ts_info in enumerate(timestamps):
+    if not timestamps:
+        return []
+
+    # Limit the number of concurrent ffmpeg processes so we don't overload the host.
+    max_workers = min(5, len(timestamps))
+
+    def _process_timestamp(idx: int, ts_info: dict) -> tuple[int, str | None]:
         camera = ts_info["camera"]
         timestamp = ts_info["timestamp"]
 
         if camera not in camera_crops:
             logger.warning(f"No crop coordinates for camera {camera}")
-            continue
+            return idx, None
 
         norm_x1, norm_y1, norm_x2, norm_y2 = camera_crops[camera]
 
@@ -516,7 +550,7 @@ def _extract_keyframes(
                 .get()
             )
         except Exception:
-            continue
+            return idx, None
 
         relative_time = timestamp - recording.start_time
 
@@ -530,38 +564,57 @@ def _extract_keyframes(
                 height=None,
             )
 
-            if image_data:
-                nparr = np.frombuffer(image_data, np.uint8)
-                img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+            if not image_data:
+                return idx, None
 
-                if img is not None:
-                    height, width = img.shape[:2]
+            nparr = np.frombuffer(image_data, np.uint8)
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
-                    x1 = int(norm_x1 * width)
-                    y1 = int(norm_y1 * height)
-                    x2 = int(norm_x2 * width)
-                    y2 = int(norm_y2 * height)
+            if img is None:
+                return idx, None
 
-                    x1_clipped = max(0, min(x1, width))
-                    y1_clipped = max(0, min(y1, height))
-                    x2_clipped = max(0, min(x2, width))
-                    y2_clipped = max(0, min(y2, height))
+            height, width = img.shape[:2]
 
-                    if x2_clipped > x1_clipped and y2_clipped > y1_clipped:
-                        cropped = img[y1_clipped:y2_clipped, x1_clipped:x2_clipped]
-                        resized = cv2.resize(cropped, (224, 224))
+            x1 = int(norm_x1 * width)
+            y1 = int(norm_y1 * height)
+            x2 = int(norm_x2 * width)
+            y2 = int(norm_y2 * height)
 
-                        output_path = os.path.join(output_dir, f"frame_{idx:04d}.jpg")
-                        cv2.imwrite(output_path, resized)
-                        keyframe_paths.append(output_path)
+            x1_clipped = max(0, min(x1, width))
+            y1_clipped = max(0, min(y1, height))
+            x2_clipped = max(0, min(x2, width))
+            y2_clipped = max(0, min(y2, height))
 
+            if x2_clipped <= x1_clipped or y2_clipped <= y1_clipped:
+                return idx, None
+
+            cropped = img[y1_clipped:y2_clipped, x1_clipped:x2_clipped]
+            resized = cv2.resize(cropped, (224, 224))
+
+            output_path = os.path.join(output_dir, f"frame_{idx:04d}.jpg")
+            cv2.imwrite(output_path, resized)
+            return idx, output_path
         except Exception as e:
             logger.debug(
                 f"Failed to extract frame from {recording.path} at {relative_time}s: {e}"
             )
-            continue
+            return idx, None
 
-    return keyframe_paths
+    keyframes_with_index: list[tuple[int, str]] = []
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        future_to_idx = {
+            executor.submit(_process_timestamp, idx, ts_info): idx
+            for idx, ts_info in enumerate(timestamps)
+        }
+
+        for future in as_completed(future_to_idx):
+            _, path = future.result()
+            if path:
+                keyframes_with_index.append((future_to_idx[future], path))
+
+    keyframes_with_index.sort(key=lambda item: item[0])
+    return [path for _, path in keyframes_with_index]
 
 
 def _select_distinct_images(

frigate/util/process.py

@@ -65,10 +65,15 @@ class FrigateProcess(BaseProcess):
         logging.basicConfig(handlers=[], force=True)
         logging.getLogger().addHandler(QueueHandler(self.__log_queue))
 
+        # Always apply base log level suppressions for noisy third-party libraries
+        # even if no specific logConfig is provided
         if logConfig:
             frigate.log.apply_log_levels(
                 logConfig.default.value.upper(), logConfig.logs
             )
+        else:
+            # Apply default INFO level with standard library suppressions
+            frigate.log.apply_log_levels("INFO", {})
 
         self._setup_memray()
 

frigate/util/rknn_converter.py

@@ -8,6 +8,7 @@ import time
 from pathlib import Path
 from typing import Optional
 
+from frigate.const import SUPPORTED_RK_SOCS
 from frigate.util.file import FileLock
 
 logger = logging.getLogger(__name__)
@@ -68,9 +69,20 @@ def is_rknn_compatible(model_path: str, model_type: str | None = None) -> bool:
         True if the model is RKNN-compatible, False otherwise
     """
     soc = get_soc_type()
+
     if soc is None:
         return False
 
+    # Check if the SoC is actually a supported RK device
+    # This prevents false positives on non-RK devices (e.g., macOS Docker)
+    # where /proc/device-tree/compatible might exist but contain non-RK content
+    if soc not in SUPPORTED_RK_SOCS:
+        logger.debug(
+            f"SoC '{soc}' is not a supported RK device for RKNN conversion. "
+            f"Supported SoCs: {SUPPORTED_RK_SOCS}"
+        )
+        return False
+
     if not model_type:
         model_type = get_rknn_model_type(model_path)
 

web/public/locales/ca/components/filter.json

@@ -132,5 +132,9 @@
     },
     "count_one": "{{count}} Classe",
     "count_other": "{{count}} Classes"
+  },
+  "attributes": {
+    "label": "Atributs de classificació",
+    "all": "Tots els atributs"
   }
 }

web/public/locales/ca/views/classificationModel.json

@@ -54,7 +54,7 @@
   },
   "renameCategory": {
     "title": "Reanomena la classe",
-    "desc": "Introduïu un nom nou per {{name}}. Se us requerirà que torneu a entrenar el model per al canvi de nom a afectar."
+    "desc": "Introduïu un nom nou per {{name}}. Se us requerirà que torneu a entrenar el model per al canvi de nom afectar."
   },
   "description": {
     "invalidName": "Nom no vàlid. Els noms només poden incloure lletres, números, espais, apòstrofs, guions baixos i guions."
@@ -116,7 +116,8 @@
         "classesUnique": "Els noms de classe han de ser únics",
         "stateRequiresTwoClasses": "Els models d'estat requereixen almenys 2 classes",
         "objectLabelRequired": "Seleccioneu una etiqueta d'objecte",
-        "objectTypeRequired": "Seleccioneu un tipus de classificació"
+        "objectTypeRequired": "Seleccioneu un tipus de classificació",
+        "noneNotAllowed": "La classe 'none' no està permesa"
       },
       "states": "Estats"
     },
@@ -172,7 +173,9 @@
     "states": "Estats"
   },
   "details": {
-    "scoreInfo": "La puntuació representa la confiança mitjana de la classificació en totes les deteccions d'aquest objecte."
+    "scoreInfo": "La puntuació representa la confiança mitjana de la classificació en totes les deteccions d'aquest objecte.",
+    "none": "Cap",
+    "unknown": "Desconegut"
   },
   "edit": {
     "title": "Edita el model de classificació",

web/public/locales/ca/views/explore.json

@@ -100,13 +100,15 @@
           "updatedSublabel": "Subetiqueta actualitzada amb èxit.",
           "updatedLPR": "Matrícula actualitzada amb èxit.",
           "regenerate": "El {{provider}} ha sol·licitat una nova descripció. En funció de la velocitat del vostre proveïdor, la nova descripció pot trigar un temps a regenerar-se.",
-          "audioTranscription": "S'ha sol·licitat correctament la transcripció d'àudio. Depenent de la velocitat del vostre servidor Frigate, la transcripció pot trigar una estona a completar-se."
+          "audioTranscription": "S'ha sol·licitat correctament la transcripció d'àudio. Depenent de la velocitat del vostre servidor Frigate, la transcripció pot trigar una estona a completar-se.",
+          "updatedAttributes": "Els atributs s'han actualitzat correctament."
         },
         "error": {
           "regenerate": "No s'ha pogut contactar amb {{provider}} per obtenir una nova descripció: {{errorMessage}}",
           "updatedSublabelFailed": "No s'ha pogut actualitzar la subetiqueta: {{errorMessage}}",
           "updatedLPRFailed": "No s'ha pogut actualitzar la matrícula: {{errorMessage}}",
-          "audioTranscription": "Error en demanar la transcripció d'audio {{errorMessage}}"
+          "audioTranscription": "Error en demanar la transcripció d'audio {{errorMessage}}",
+          "updatedAttributesFailed": "No s'han pogut actualitzar els atributs: {{errorMessage}}"
         }
       },
       "title": "Revisar detalls de l'element",
@@ -162,7 +164,12 @@
     },
     "score": {
       "label": "Puntuació"
-    }
+    },
+    "editAttributes": {
+      "title": "Edita els atributs",
+      "desc": "Seleccioneu els atributs de classificació per a aquesta {{label}}"
+    },
+    "attributes": "Atributs de classificació"
   },
   "searchResult": {
     "tooltip": "S'ha identificat {{type}} amb una confiança del {{confidence}}%",

web/public/locales/ca/views/search.json

@@ -15,7 +15,8 @@
       "max_speed": "Velocitat màxima",
       "recognized_license_plate": "Matrícula reconeguda",
       "has_clip": "Té Clip",
-      "has_snapshot": "Té instantània"
+      "has_snapshot": "Té instantània",
+      "attributes": "Atributs"
     },
     "searchType": {
       "thumbnail": "Miniatura",

web/public/locales/ca/views/settings.json

@@ -484,7 +484,7 @@
   "users": {
     "table": {
       "username": "Usuari",
-      "password": "Contrasenya",
+      "password": "Restableix la contrasenya",
       "deleteUser": "Suprimir usuari",
       "noUsers": "No s'han trobat usuaris.",
       "changeRole": "Canviar la funció d’usuari",
@@ -595,7 +595,7 @@
       "title": "Gestió d'usuaris",
       "desc": "Gestioneu els comptes d'usuari d'aquesta instància de Frigate."
     },
-    "updatePassword": "Actualitzar contrasenya"
+    "updatePassword": "Restableix la contrasenya"
   },
   "frigatePlus": {
     "snapshotConfig": {
@@ -696,7 +696,7 @@
       "title": "Classificació d'ocells",
       "desc": "La classificació d’ocells identifica ocells coneguts mitjançant un model TensorFlow quantitzat. Quan es reconeix un ocell conegut, el seu nom comú s’afegeix com a subetiqueta. Aquesta informació es mostra a la interfície d’usuari, als filtres i també a les notificacions."
     },
-    "title": "Parmàmetres complementaris",
+    "title": "Configuració dels enriquiments",
     "toast": {
       "error": "No s'han pogut guardar els canvis de configuració: {{errorMessage}}",
       "success": "Els paràmetres complementaris s'han desat. Reinicia Frigate per aplicar els canvis."
@@ -805,7 +805,7 @@
     "documentTitle": "Disparadors",
     "management": {
       "title": "Activadors",
-      "desc": "Gestionar els disparadors de {{camera}}. Usa les tipus de miniatures per disparar miniatures similars a l'objecte a seguir seleccionat, i el tipus de descripció per disparar en cas de descripcions similars a l'especificada."
+      "desc": "Gestionar els disparadors de {{camera}}. Usa els tipus de miniatures per disparar miniatures similars a l'objecte a seguir seleccionat, i el tipus de descripció per disparar en cas de descripcions similars a l'especificada."
     },
     "addTrigger": "Afegir disaprador",
     "semanticSearch": {

web/public/locales/ca/views/system.json

@@ -190,7 +190,10 @@
       "review_description_events_per_second": "Descripció de la revisió",
       "object_description": "Descripció de l'objecte",
       "object_description_speed": "Velocitat de la descripció de l'objecte",
-      "object_description_events_per_second": "Descripció de l'objecte"
+      "object_description_events_per_second": "Descripció de l'objecte",
+      "classification": "{{name}} Classificació",
+      "classification_speed": "Velocitat de classificació de {{name}}",
+      "classification_events_per_second": "{{name}} Esdeveniments de classificació per segon"
     },
     "infPerSecond": "Inferències per segon",
     "averageInf": "Temps mitjà d'inferència"

web/public/locales/cs/views/classificationModel.json

@@ -1,34 +1,47 @@
 {
-  "documentTitle": "Klasifikační modely",
+  "documentTitle": "Klasifikační modely - Frigate",
   "button": {
     "deleteClassificationAttempts": "Odstrániť Klasifikačné obrazy",
-    "renameCategory": "Premenovať triedu",
-    "deleteCategory": "Zmazať triedu",
-    "deleteImages": "Zmazať obrázok",
-    "trainModel": "Trenovací model",
-    "addClassification": "Pridať klasifikáciu",
-    "deleteModels": "Zmazať modeli",
-    "editModel": "Upraviť model"
+    "renameCategory": "Přejmenovat třídu",
+    "deleteCategory": "Smazat třídu",
+    "deleteImages": "Smazat obrázek",
+    "trainModel": "Trénovat model",
+    "addClassification": "Přidat klasifikaci",
+    "deleteModels": "Smazat modely",
+    "editModel": "Upravit model"
   },
   "details": {
-    "scoreInfo": "Skóre predstavuje priemernú istotu klasifikácie naprieč detekciami tohoto objektu."
+    "scoreInfo": "Skóre predstavuje priemernú istotu klasifikácie naprieč detekciami tohoto objektu.",
+    "none": "Nic",
+    "unknown": "Neznámý"
   },
   "tooltip": {
-    "trainingInProgress": "Model se práve trénuje",
-    "noNewImages": "Žiadne nové obrázky na trénovanie. Najskôr klasifikujte viac obrazkov v datasete.",
-    "noChanges": "Od posledného treningu nedošlo k žiadnym zmenám v datasete.",
-    "modelNotReady": "Model nieje pripravený na trénovanie."
+    "trainingInProgress": "Model se právě trénuje",
+    "noNewImages": "Žádné obrázky pro trénování. Nejdříve klasifikujte obrázky pro dataset.",
+    "noChanges": "Od posledního trénování nedošlo k žádné změně.",
+    "modelNotReady": "Model není připravený na trénování."
   },
   "toast": {
     "success": {
-      "deletedImage": "Zmazať obrazky",
-      "deletedModel_one": "Úspešne odstranený {{count}} model",
-      "deletedModel_few": "Úspešne odstranené {{count}} modely",
-      "deletedModel_other": "Úspěšne ostranených {{count}} modelov",
-      "deletedCategory": "Zmazať triedu",
+      "deletedImage": "Smazat obrázky",
+      "deletedModel_one": "Úspěšně odstraněný {{count}} model",
+      "deletedModel_few": "Úspěšně odstraněné {{count}} modely",
+      "deletedModel_other": "Úspěšně odstraněných {{count}} modelů",
+      "deletedCategory": "Smazat třídu",
       "categorizedImage": "Obrázek úspěšně klasifikován",
       "trainedModel": "Úspěšně vytrénovaný model.",
-      "trainingModel": "Trénování modelu bylo úspěšně zahájeno."
+      "trainingModel": "Trénování modelu bylo úspěšně zahájeno.",
+      "updatedModel": "Konfigurace modelu úspěšně aktualizována.",
+      "renamedCategory": "Třída úspěšně přejmenována na {{name}}"
+    },
+    "error": {
+      "deleteImageFailed": "Chyba při mazání: {{errorMessage}}",
+      "deleteCategoryFailed": "Chyba při mazání třídy: {{errorMessage}}",
+      "deleteModelFailed": "Chyba při mazání modelu: {{errorMessage}}",
+      "categorizeFailed": "Chyba při mazání obrázku: {{errorMessage}}"
     }
+  },
+  "train": {
+    "titleShort": "Nedávný"
   }
 }

web/public/locales/cs/views/events.json

@@ -43,6 +43,7 @@
     "label": "Detail",
     "noDataFound": "Žádná detailní data k prohlédnutí",
     "aria": "Přepnout detailní zobrazení",
-    "trackedObject_other": "{{count}} objektů"
+    "trackedObject_other": "{{count}} objektů",
+    "trackedObject_one": "{{count}} objektů"
   }
 }

web/public/locales/cs/views/faceLibrary.json

@@ -38,7 +38,8 @@
   "train": {
     "title": "Nedávná rozpoznání",
     "empty": "Nejsou zde žádné předchozí pokusy o rozpoznání obličeje",
-    "aria": "Vybrat trénink"
+    "aria": "Vybrat poslední rozpoznávání",
+    "titleShort": "Nedávný"
   },
   "description": {
     "addFace": "Přidejte novou kolekci do Knihovny obličejů nahráním prvního obrázku.",
@@ -76,7 +77,7 @@
       "deletedName_one": "{{count}} obličej byl úspěšně odstraněn.",
       "deletedName_few": "{{count}} tváře byly úspěšně odstraněny.",
       "deletedName_other": "{{count}} tváře byly úspěšně odstraněny.",
-      "updatedFaceScore": "Úspěšně aktualizováno skóre obličeje.",
+      "updatedFaceScore": "Úspěšně aktualizováno skóre obličeje na {{name}} ({{score}}).",
       "addFaceLibrary": "{{name}} byl(a) úspěšně přidán(a) do Knihovny obličejů!"
     },
     "error": {

web/public/locales/cs/views/search.json

@@ -26,7 +26,8 @@
       "min_score": "Minimální Skóre",
       "recognized_license_plate": "Rozpoznaná SPZ",
       "has_clip": "Má Klip",
-      "has_snapshot": "Má Snímek"
+      "has_snapshot": "Má Snímek",
+      "attributes": "Atributy"
     },
     "tips": {
       "desc": {

web/public/locales/cs/views/settings.json

@@ -8,7 +8,7 @@
     "masksAndZones": "Editor masky a zón - Frigate",
     "motionTuner": "Ladění detekce pohybu - Frigate",
     "object": "Ladění - Frigate",
-    "general": "Nastavení rozhraní- Frigate",
+    "general": "Nastavení rozhraní - Frigate",
     "frigatePlus": "Frigate+ nastavení - Frigate",
     "enrichments": "Nastavení obohacení - Frigate",
     "cameraManagement": "Správa kamer - Frigate",

web/public/locales/da/components/player.json

@@ -1,5 +1,5 @@
 {
-  "noRecordingsFoundForThisTime": "Ingen optagelser fundet i det angivet tidsrum",
+  "noRecordingsFoundForThisTime": "Ingen optagelser fundet i det angivne tidsrum",
   "noPreviewFound": "Ingen forhåndsvisning fundet",
   "cameraDisabled": "Kamera er deaktiveret",
   "noPreviewFoundFor": "Ingen forhåndsvisning fundet for {{cameraName}}",

web/public/locales/da/views/classificationModel.json

@@ -1,7 +1,8 @@
 {
   "documentTitle": "Klassifikationsmodeller",
   "details": {
-    "scoreInfo": "Scoren repræsenterer den gennemsnitlige klassifikationssikkerhed på tværs af alle registreringer af dette objekt."
+    "scoreInfo": "Scoren repræsenterer den gennemsnitlige klassifikationssikkerhed på tværs af alle registreringer af dette objekt.",
+    "unknown": "Ukendt"
   },
   "description": {
     "invalidName": "Ugyldigt navn. Navne må kun indeholde bogstaver, tal, mellemrum, apostroffer, understregninger og bindestreger."

web/public/locales/de/common.json

@@ -12,7 +12,7 @@
     "24hours": "24 Stunden",
     "month_one": "{{time}} Monat",
     "month_other": "{{time}} Monate",
-    "d": "{{time}} Tag",
+    "d": "{{time}} Tg.",
     "day_one": "{{time}} Tag",
     "day_other": "{{time}} Tage",
     "m": "{{time}} Min",
@@ -37,12 +37,12 @@
     "30minutes": "30 Minuten",
     "1hour": "1 Stunde",
     "lastWeek": "Letzte Woche",
-    "h": "{{time}} Stunde",
+    "h": "{{time}} Std.",
     "ago": "vor {{timeAgo}}",
     "untilRestart": "Bis zum Neustart",
     "justNow": "Gerade",
     "pm": "nachmittags",
-    "mo": "{{time}}Monat",
+    "mo": "{{time}} Mon.",
     "formattedTimestamp": {
       "12hour": "d. MMM, hh:mm:ss aaa",
       "24hour": "dd. MMM, hh:mm:ss aaa"
@@ -82,7 +82,7 @@
       "12hour": "d. MMM yyyy",
       "24hour": "d. MMM yyyy"
     },
-    "inProgress": "In Bearbeitung",
+    "inProgress": "Im Gange",
     "invalidStartTime": "Ungültige Startzeit",
     "invalidEndTime": "Ungültige Endzeit"
   },

web/public/locales/de/components/filter.json

@@ -132,5 +132,9 @@
     },
     "count_one": "{{count}} Klasse",
     "count_other": "{{count}} Klassen"
+  },
+  "attributes": {
+    "label": "Klassifizierungsattribute",
+    "all": "Alle Attribute"
   }
 }

web/public/locales/de/components/player.json

@@ -24,7 +24,7 @@
       "title": "Latenz:",
       "value": "{{seconds}} Sekunden",
       "short": {
-        "title": "Lazenz",
+        "title": "Latenz",
         "value": "{{seconds}} s"
       }
     },

web/public/locales/de/views/classificationModel.json

@@ -1,7 +1,9 @@
 {
   "documentTitle": "Klassifizierungsmodelle - Fregatte",
   "details": {
-    "scoreInfo": "Die Punktzahl gibt die durchschnittliche Konfidenz aller Erkennungen dieses Objekts wieder."
+    "scoreInfo": "Die Punktzahl gibt die durchschnittliche Konfidenz aller Erkennungen dieses Objekts wieder.",
+    "none": "Keiner",
+    "unknown": "Unbekannt"
   },
   "button": {
     "deleteClassificationAttempts": "Lösche klassifizierte Bilder",
@@ -17,7 +19,7 @@
     "trainingInProgress": "Modell wird gerade trainiert",
     "noNewImages": "Keine weiteren Bilder zum trainieren. Bitte klassifiziere weitere Bilder im Datensatz.",
     "noChanges": "Keine Veränderungen des Datensatzes seit dem letzten Training.",
-    "modelNotReady": "Modell ist nicht bereit zum Training"
+    "modelNotReady": "Modell ist nicht bereit für das Training"
   },
   "toast": {
     "success": {
@@ -130,7 +132,7 @@
       "classesTip": "Über Klassen lernen",
       "classesStateDesc": "Definieren Sie die verschiedenen Zustände, in denen sich Ihr Kamerabereich befinden kann. Beispiel: „offen” und „geschlossen” für ein Garagentor.",
       "classesObjectDesc": "Definieren Sie die verschiedenen Kategorien, in die erkannte Objekte klassifiziert werden sollen. Beispiel: „Lieferant“, „Bewohner“, „Fremder“ für die Klassifizierung von Personen.",
-      "classPlaceholder": "Eingabe Klassenbezeichnung...",
+      "classPlaceholder": "Klassenbezeichnung eingeben...",
       "errors": {
         "nameRequired": "Modellname ist erforderlich",
         "nameLength": "Der Modellname darf maximal 64 Zeichen lang sein",
@@ -139,7 +141,8 @@
         "classesUnique": "Klassenname muss eindeutig sein",
         "stateRequiresTwoClasses": "Gebietsmodelle erfordern mindestens zwei Klassen",
         "objectLabelRequired": "Bitte wähle eine Objektbeschriftung",
-        "objectTypeRequired": "Bitte wählen Sie einen Klassifizierungstyp aus"
+        "objectTypeRequired": "Bitte wählen Sie einen Klassifizierungstyp aus",
+        "noneNotAllowed": "Die Klasse „none“ ist nicht zulässig"
       }
     },
     "step2": {

web/public/locales/de/views/explore.json

@@ -18,13 +18,15 @@
           "updatedSublabel": "Unterkategorie erfolgreich aktualisiert.",
           "updatedLPR": "Nummernschild erfolgreich aktualisiert.",
           "regenerate": "Eine neue Beschreibung wurde von {{provider}} angefordert. Je nach Geschwindigkeit des Anbieters kann es einige Zeit dauern, bis die neue Beschreibung generiert ist.",
-          "audioTranscription": "Die Audio-Transkription wurde erfolgreich angefordert. Je nach Geschwindigkeit Ihres Frigate-Servers kann die Transkription einige Zeit in Anspruch nehmen."
+          "audioTranscription": "Die Audio-Transkription wurde erfolgreich angefordert. Je nach Geschwindigkeit Ihres Frigate-Servers kann die Transkription einige Zeit in Anspruch nehmen.",
+          "updatedAttributes": "Attribute erfolgreich aktualisiert."
         },
         "error": {
           "regenerate": "Der Aufruf von {{provider}} für eine neue Beschreibung ist fehlgeschlagen: {{errorMessage}}",
           "updatedSublabelFailed": "Untekategorie konnte nicht aktualisiert werden: {{errorMessage}}",
           "updatedLPRFailed": "Aktualisierung des Kennzeichens fehlgeschlagen: {{errorMessage}}",
-          "audioTranscription": "Die Anforderung der Audio Transkription ist fehlgeschlagen: {{errorMessage}}"
+          "audioTranscription": "Die Anforderung der Audio Transkription ist fehlgeschlagen: {{errorMessage}}",
+          "updatedAttributesFailed": "Attribute konnten nicht aktualisiert werden: {{errorMessage}}"
         }
       }
     },
@@ -72,7 +74,12 @@
     },
     "score": {
       "label": "Ergebnis"
-    }
+    },
+    "editAttributes": {
+      "title": "Attribute bearbeiten",
+      "desc": "Wählen Sie Klassifizierungsattribute für dieses {{label}} aus"
+    },
+    "attributes": "Klassifizierungsattribute"
   },
   "documentTitle": "Erkunde - Frigate",
   "generativeAI": "Generative KI",
@@ -254,7 +261,7 @@
         "faceOrLicense_plate": "{{attribute}} erkannt für {{label}}",
         "other": "{{label}} erkannt als {{attribute}}"
       },
-      "gone": "{{label}} hat verlassen",
+      "gone": "{{label}} hat sich entfernt",
       "heard": "{{label}} wurde gehört",
       "external": "{{label}} erkannt",
       "header": {

web/public/locales/de/views/faceLibrary.json

@@ -44,7 +44,7 @@
     "deleteFace": "Lösche Gesicht"
   },
   "train": {
-    "title": "Kürzliche Erkennungen",
+    "title": "Neueste Erkennungen",
     "aria": "Wähle aktuelle Erkennungen",
     "empty": "Es gibt keine aktuellen Versuche zur Gesichtserkennung",
     "titleShort": "frisch"

web/public/locales/de/views/search.json

@@ -25,7 +25,8 @@
       "max_speed": "Maximalgeschwindigkeit",
       "time_range": "Zeitraum",
       "labels": "Labels",
-      "sub_labels": "Unterlabels"
+      "sub_labels": "Unterlabels",
+      "attributes": "Attribute"
     },
     "toast": {
       "error": {

web/public/locales/de/views/settings.json

@@ -5,7 +5,7 @@
     "camera": "Kameraeinstellungen - Frigate",
     "masksAndZones": "Masken- und Zoneneditor – Frigate",
     "object": "Debug - Frigate",
-    "general": "UI-Einstellungen – Frigate",
+    "general": "UI-Einstellungen - Frigate",
     "frigatePlus": "Frigate+ Einstellungen – Frigate",
     "classification": "Klassifizierungseinstellungen – Frigate",
     "motionTuner": "Bewegungserkennungs-Optimierer – Frigate",
@@ -490,7 +490,7 @@
   },
   "users": {
     "addUser": "Benutzer hinzufügen",
-    "updatePassword": "Passwort aktualisieren",
+    "updatePassword": "Passwort zurücksetzen",
     "toast": {
       "success": {
         "deleteUser": "Benutzer {{user}} wurde erfolgreich gelöscht",
@@ -514,7 +514,7 @@
       "changeRole": "Benutzerrolle ändern",
       "deleteUser": "Benutzer löschen",
       "noUsers": "Keine Benutzer gefunden.",
-      "password": "Passwort",
+      "password": "Passwort zurücksetzen",
       "username": "Benutzername",
       "actions": "Aktionen",
       "role": "Rolle"
@@ -599,7 +599,8 @@
         "currentPasswordRequired": "Aktuelles Passwort wird benötigt",
         "incorrectCurrentPassword": "Aktuelles Passwort ist falsch",
         "passwordVerificationFailed": "Passwort konnte nicht überprüft werden",
-        "multiDeviceWarning": "Alle anderen Geräte, auf denen Sie angemeldet sind, müssen sich innerhalb von {{refresh_time}} erneut anmelden. Sie können auch alle Benutzer dazu zwingen, sich sofort erneut zu authentifizieren, indem Sie Ihr JWT-Geheimnis rotieren."
+        "multiDeviceWarning": "Alle anderen Geräte, auf denen Sie angemeldet sind, müssen sich innerhalb von {{refresh_time}} erneut anmelden.",
+        "multiDeviceAdmin": "Sie können auch alle Benutzer dazu zwingen, sich sofort erneut zu authentifizieren, indem Sie Ihr JWT-Geheimnis ändern."
       }
     }
   },
@@ -704,8 +705,8 @@
   },
   "enrichments": {
     "birdClassification": {
-      "title": "Vogel Klassifizierung",
-      "desc": "Die Vogelklassifizierung identifiziert bekannte Vögel mithilfe eines quantisierten Tensorflow-Modells. Wenn ein bekannter Vogel erkannt wird, wird sein allgemeiner Name als sub_label hinzugefügt. Diese Informationen sind in der Benutzeroberfläche, in Filtern und in Benachrichtigungen enthalten."
+      "title": "Vogelerkennung",
+      "desc": "Die Vogelerkennung identifiziert Vögelarten mithilfe eines quantisierten Tensorflowmodells. Wenn eine Vogelart erkannt wird, wird ihr Name als sub_label hinzugefügt. Diese Informationen sind in der Benutzeroberfläche, in Filtern und in Benachrichtigungen enthalten."
     },
     "title": "Anreicherungseinstellungen",
     "unsavedChanges": "Ungesicherte geänderte Verbesserungseinstellungen",
@@ -729,7 +730,7 @@
         "desc": "Die Größe des für die Einbettung der semantischen Suche verwendeten Modells.",
         "large": {
           "title": "groß",
-          "desc": "Bei der Verwendung von <em>groß</em> wird das gesamte Jina-Modell verwendet und automatisch auf der GPU ausgeführt, falls zutreffend."
+          "desc": "Bei der Verwendung von <em>groß</em> wird das gesamte Jina-Modell verwendet und automatisch auf der GPU ausgeführt, falls möglich."
         }
       },
       "title": "Semantische Suche",
@@ -738,7 +739,7 @@
     },
     "faceRecognition": {
       "title": "Gesichtserkennung",
-      "desc": "Die Gesichtserkennung ermöglicht es, Personen Namen zuzuweisen, und wenn ihr Gesicht erkannt wird, ordnet Frigate den Namen der Person als Untertitel zu. Diese Informationen sind in der Benutzeroberfläche, den Filtern und in den Benachrichtigungen enthalten.",
+      "desc": "Die Gesichtserkennung ermöglicht es, Personen Namen zuzuweisen. Wenn ein Gesicht erkannt wird, ordnet Frigate den Namen der Person als Untertitel zu. Diese Informationen sind in der Benutzeroberfläche, den Filtern und in den Benachrichtigungen enthalten.",
       "readTheDocumentation": "Lies die Dokumentation",
       "modelSize": {
         "label": "Modellgröße",
@@ -1095,7 +1096,7 @@
       }
     },
     "step3": {
-      "description": "Konfigurieren Sie Stream-Rollen und fügen Sie zusätzliche Streams für Ihre Kamera hinzu",
+      "description": "Konfigurieren Sie Stream-Rollen und fügen Sie zusätzliche Streams für Ihre Kamera hinzu.",
       "validationTitle": "Stream Validierung",
       "connectAllStreams": "Verbinde alle Streams",
       "reconnectionSuccess": "Wiederverbindung erfolgreich.",
@@ -1208,7 +1209,8 @@
         "audioCodecRequired": "Ein Audiostream ist erforderlich, um die Audioerkennung zu unterstützen.",
         "restreamingWarning": "Die Reduzierung der Verbindungen zur Kamera für den Aufzeichnungsstream kann zu einer geringfügigen Erhöhung der CPU-Auslastung führen.",
         "brands": {
-          "reolink-rtsp": "Reolink RTSP wird nicht empfohlen. Aktivieren Sie HTTP in den Firmware-Einstellungen der Kamera und starten Sie den Assistenten neu."
+          "reolink-rtsp": "Reolink RTSP wird nicht empfohlen. Aktivieren Sie HTTP in den Firmware-Einstellungen der Kamera und starten Sie den Assistenten neu.",
+          "reolink-http": "Für Reolink-HTTP-Streams sollten sie FFmpeg verwenden, um eine bessere Kompatibilität zu gewährleisten. Aktivieren Sie für diesen Stream die Option „Stream-Kompatibilitätsmodus verwenden“."
         },
         "dahua": {
           "substreamWarning": "Substream 1 ist auf eine niedrige Auflösung festgelegt. Viele Kameras von Dahua / Amcrest / EmpireTech unterstützen zusätzliche Substreams, die in den Kameraeinstellungen aktiviert werden müssen. Es wird empfohlen, diese Streams zu überprüfen und zu nutzen, sofern sie verfügbar sind."
@@ -1224,7 +1226,7 @@
     "addCamera": "Neue Kamera hinzufügen",
     "editCamera": "Kamera bearbeiten:",
     "selectCamera": "Wähle eine Kamera",
-    "backToSettings": "Zurück zu Kamera-Einstellungen",
+    "backToSettings": "Zurück zu Kameraeinstellungen",
     "streams": {
       "title": "Kameras aktivieren / deaktivieren",
       "desc": "Deaktiviere eine Kamera vorübergehend, bis Frigate neu gestartet wird. Deaktivierung einer Kamera stoppt die Verarbeitung der Streams dieser Kamera durch Frigate vollständig. Erkennung, Aufzeichnung und Debugging sind dann nicht mehr verfügbar. <br /> <em>Hinweis: Dies deaktiviert nicht die go2rtc restreams.</em>"

web/public/locales/de/views/system.json

@@ -178,7 +178,10 @@
       "review_description_events_per_second": "Bewertungsbeschreibung",
       "object_description": "Objekt Beschreibung",
       "object_description_speed": "Objektbeschreibung Geschwindigkeit",
-      "object_description_events_per_second": "Objektbeschreibung"
+      "object_description_events_per_second": "Objektbeschreibung",
+      "classification": "{{name}} Klassifizierung",
+      "classification_speed": "{{name}} Klassifizierungsgeschwindigkeit",
+      "classification_events_per_second": "{{name}} Klassifizierungsereignisse pro Sekunde"
     },
     "title": "Optimierungen",
     "infPerSecond": "Rückschlüsse pro Sekunde",

web/public/locales/el/common.json

@@ -68,7 +68,14 @@
     "formattedTimestampFilename": {
       "12hour": "dd-MM-yy-h-mm-ss-a",
       "24hour": "dd-MM-yy-HH-mm-ss"
-    }
+    },
+    "d": "{{time}}η",
+    "h": "{{time}}ω",
+    "m": "{{time}}λ",
+    "s": "{{time}}δ",
+    "inProgress": "Σε εξέλιξη",
+    "invalidStartTime": "Μη έγκυρη ώρα έναρξης",
+    "invalidEndTime": "Μη έγκυρη ώρα λήξης"
   },
   "menu": {
     "live": {
@@ -100,7 +107,8 @@
     "yes": "Ναι",
     "no": "Όχι",
     "download": "Κατέβασμα",
-    "info": "Πληροφορίες"
+    "info": "Πληροφορίες",
+    "history": "Ιστορία"
   },
   "unit": {
     "speed": {
@@ -120,6 +128,54 @@
     }
   },
   "label": {
-    "back": "Επιστροφή"
+    "back": "Επιστροφή",
+    "hide": "Απόκρυψη {{item}}",
+    "show": "Εμφάνιση {{item}}",
+    "ID": "ID",
+    "none": "Κανένα",
+    "all": "Όλα"
+  },
+  "toast": {
+    "save": {
+      "title": "Αποθήκευση",
+      "error": {
+        "title": "Αποτυχία αποθήκευσης αλλαγών διαμόρφωσης: {{errorMessage}}",
+        "noMessage": "Αποτυχία αποθήκευσης αλλαγών διαμόρφωσης"
+      }
+    }
+  },
+  "role": {
+    "admin": "Διαχειριστής",
+    "desc": "Οι διαχειριστές έχουν πλήρη πρόσβαση σε όλες τις λειτουργίες του περιβάλλοντος χρήστη Frigate. Οι θεατές έχουν περιορισμένη πρόσβαση στην προβολή καμερών, στην αναθεώρηση στοιχείων και σε ιστορικό υλικό στο περιβάλλον χρήστη.",
+    "viewer": "Θεατής"
+  },
+  "pagination": {
+    "previous": {
+      "title": "Προηγούμενο",
+      "label": "Μετάβαση στην προηγούμενη σελίδα"
+    },
+    "next": {
+      "title": "Επόμενο",
+      "label": "Μετάβαση στην επόμενη σελίδα"
+    },
+    "more": "Περισσότερες σελίδες"
+  },
+  "accessDenied": {
+    "documentTitle": "Πρόσβαση απορρίφθηκε - Frigate",
+    "title": "Πρόσβαση απορρίφθηκε",
+    "desc": "Δεν έχετε άδεια να δείτε αυτή τη σελίδα."
+  },
+  "notFound": {
+    "documentTitle": "Δεν βρέθηκε - Frigate",
+    "title": "404",
+    "desc": "Η σελίδα δεν βρέθηκε"
+  },
+  "list": {
+    "two": "{{0}} και {{1}}",
+    "many": "{{items}} και {{last}}",
+    "separatorWithSpace": ", "
+  },
+  "field": {
+    "internalID": "Το εσωτερικό ID που χρησιμοποίησε η Fregate στη διαμόρφωση και τη βάση δεδομένων"
   }
 }

web/public/locales/el/components/dialog.json

@@ -46,5 +46,10 @@
     "toast": {
       "success": "Επιτυχής έναρξη εξαγωγής. Δείτε το αρχείο στον φάκελο /exports."
     }
+  },
+  "search": {
+    "saveSearch": {
+      "label": "Αποθήκευση αναζήτησης"
+    }
   }
 }

web/public/locales/el/views/explore.json

@@ -42,5 +42,11 @@
     "noImageFound": "Δεν βρέθηκε εικόνα για αυτό το χρονικό σημείο."
   },
   "trackedObjectsCount_one": "{{count}} παρακολουθούμενο αντικείμενο ",
-  "trackedObjectsCount_other": "{{count}} παρακολουθούμενα αντικείμενα "
+  "trackedObjectsCount_other": "{{count}} παρακολουθούμενα αντικείμενα ",
+  "itemMenu": {
+    "downloadVideo": {
+      "label": "Λήψη βίντεο",
+      "aria": "Λήψη βίντεο"
+    }
+  }
 }

web/public/locales/el/views/exports.json

@@ -13,5 +13,10 @@
     "error": {
       "renameExportFailed": "Αποτυχία μετονομασίας εξαγωγής:{{errorMessage}}"
     }
+  },
+  "tooltip": {
+    "shareExport": "Κοινή χρήση εξαγωγής",
+    "downloadVideo": "Λήψη βίντεο",
+    "deleteExport": "Διαγραφή εξαγωγής"
   }
 }

web/public/locales/el/views/faceLibrary.json

@@ -1,6 +1,6 @@
 {
   "description": {
-    "addFace": "Οδηγός για την προσθήκη μιας νέας συλλογής στη Βιβλιοθήκη Προσώπων.",
+    "addFace": "Προσθέστε μια νέα συλλογή στη Βιβλιοθήκη Προσώπων ανεβάζοντας την πρώτη σας εικόνα.",
     "placeholder": "Εισαγάγετε ένα όνομα για αυτήν τη συλλογή",
     "invalidName": "Μη έγκυρο όνομα. Τα ονόματα μπορούν να περιλαμβάνουν γράμματα, αριθμούς, κενό διάστημα, απόστροφο, παύλα, κάτω παύλα."
   },
@@ -27,7 +27,8 @@
   },
   "documentTitle": "Βιβλιοθήκη προσώπων - Frigate",
   "uploadFaceImage": {
-    "title": "Μεταφόρτωση Εικόνας Προσώπου"
+    "title": "Μεταφόρτωση Εικόνας Προσώπου",
+    "desc": "Ανεβάστε μια εικόνα για να σαρώσετε πρόσωπα και να τα συμπεριλάβετε στο {{pageToggle}}"
   },
   "steps": {
     "nextSteps": "Επόμενα βήματα",

web/public/locales/el/views/settings.json

@@ -7,9 +7,11 @@
     "masksAndZones": "Ρυθμίσεις Μασκών και Ζωνών - Frigate",
     "motionTuner": "Ρύθμιση Κίνησης - Frigate",
     "object": "Επίλυση σφαλμάτων - Frigate",
-    "general": "Γενικές ρυθμίσεις - Frigate",
+    "general": "Ρυθμίσεις UI - Frigate",
     "frigatePlus": "Ρυθμίσεις Frigate+ - Frigate",
-    "notifications": "Ρυθμίσεις Ειδοποιήσεων"
+    "notifications": "Ρυθμίσεις Ειδοποιήσεων",
+    "cameraManagement": "Διαχείριση καμερών - Frigate",
+    "cameraReview": "Ρυθμίσεις αξιολόγησης κάμερας - Frigate"
   },
   "masksAndZones": {
     "zones": {

web/public/locales/en/components/filter.json

@@ -38,6 +38,10 @@
     "label": "Sub Labels",
     "all": "All Sub Labels"
   },
+  "attributes": {
+    "label": "Classification Attributes",
+    "all": "All Attributes"
+  },
   "score": "Score",
   "estimatedSpeed": "Estimated Speed ({{unit}})",
   "features": {

web/public/locales/en/views/classificationModel.json

@@ -1,7 +1,9 @@
 {
   "documentTitle": "Classification Models - Frigate",
   "details": {
-    "scoreInfo": "Score represents the average classification confidence across all detections of this object."
+    "scoreInfo": "Score represents the average classification confidence across all detections of this object.",
+    "none": "None",
+    "unknown": "Unknown"
   },
   "button": {
     "deleteClassificationAttempts": "Delete Classification Images",
@@ -72,7 +74,7 @@
   },
   "renameCategory": {
     "title": "Rename Class",
-    "desc": "Enter a new name for {{name}}. You will be required to retrain the model for the name change to take affect."
+    "desc": "Enter a new name for {{name}}. You will be required to retrain the model for the name change to take effect."
   },
   "description": {
     "invalidName": "Invalid name. Names can only include letters, numbers, spaces, apostrophes, underscores, and hyphens."
@@ -83,7 +85,6 @@
     "aria": "Select Recent Classifications"
   },
   "categories": "Classes",
-  "none": "None",
   "createCategory": {
     "new": "Create New Class"
   },
@@ -138,6 +139,7 @@
         "nameOnlyNumbers": "Model name cannot contain only numbers",
         "classRequired": "At least 1 class is required",
         "classesUnique": "Class names must be unique",
+        "noneNotAllowed": "The class 'none' is not allowed",
         "stateRequiresTwoClasses": "State models require at least 2 classes",
         "objectLabelRequired": "Please select an object label",
         "objectTypeRequired": "Please select a classification type"

web/public/locales/en/views/explore.json

@@ -104,12 +104,14 @@
           "regenerate": "A new description has been requested from {{provider}}. Depending on the speed of your provider, the new description may take some time to regenerate.",
           "updatedSublabel": "Successfully updated sub label.",
           "updatedLPR": "Successfully updated license plate.",
+          "updatedAttributes": "Successfully updated attributes.",
           "audioTranscription": "Successfully requested audio transcription. Depending on the speed of your Frigate server, the transcription may take some time to complete."
         },
         "error": {
           "regenerate": "Failed to call {{provider}} for a new description: {{errorMessage}}",
           "updatedSublabelFailed": "Failed to update sub label: {{errorMessage}}",
           "updatedLPRFailed": "Failed to update license plate: {{errorMessage}}",
+          "updatedAttributesFailed": "Failed to update attributes: {{errorMessage}}",
           "audioTranscription": "Failed to request audio transcription: {{errorMessage}}"
         }
       }
@@ -125,6 +127,10 @@
       "desc": "Enter a new license plate value for this {{label}}",
       "descNoLabel": "Enter a new license plate value for this tracked object"
     },
+    "editAttributes": {
+      "title": "Edit attributes",
+      "desc": "Select classification attributes for this {{label}}"
+    },
     "snapshotScore": {
       "label": "Snapshot Score"
     },
@@ -136,6 +142,7 @@
       "label": "Score"
     },
     "recognizedLicensePlate": "Recognized License Plate",
+    "attributes": "Classification Attributes",
     "estimatedSpeed": "Estimated Speed",
     "objects": "Objects",
     "camera": "Camera",

web/public/locales/en/views/search.json

@@ -16,6 +16,7 @@
       "labels": "Labels",
       "zones": "Zones",
       "sub_labels": "Sub Labels",
+      "attributes": "Attributes",
       "search_type": "Search Type",
       "time_range": "Time Range",
       "before": "Before",

web/public/locales/en/views/settings.json

@@ -679,7 +679,7 @@
       "desc": "Manage this Frigate instance's user accounts."
     },
     "addUser": "Add User",
-    "updatePassword": "Update Password",
+    "updatePassword": "Reset Password",
     "toast": {
       "success": {
         "createUser": "User {{user}} created successfully",
@@ -700,7 +700,7 @@
       "role": "Role",
       "noUsers": "No users found.",
       "changeRole": "Change user role",
-      "password": "Password",
+      "password": "Reset Password",
       "deleteUser": "Delete user"
     },
     "dialog": {

web/public/locales/en/views/system.json

@@ -192,7 +192,10 @@
       "review_description_events_per_second": "Review Description",
       "object_description": "Object Description",
       "object_description_speed": "Object Description Speed",
-      "object_description_events_per_second": "Object Description"
+      "object_description_events_per_second": "Object Description",
+      "classification": "{{name}} Classification",
+      "classification_speed": "{{name}} Classification Speed",
+      "classification_events_per_second": "{{name}} Classification Events Per Second"
     }
   }
 }

web/public/locales/et/audio.json

@@ -24,5 +24,36 @@
   "clapping": "Käteplagin",
   "finger_snapping": "Sõrmede naksutamine",
   "hands": "Käed",
-  "camera": "Kaamera"
+  "camera": "Kaamera",
+  "speech": "Kõne",
+  "babbling": "Lobisemine",
+  "yell": "Karjumine",
+  "bellow": "Röökimine",
+  "whoop": "Kisamine",
+  "whispering": "Sosistamine",
+  "laughter": "Naermine",
+  "snicker": "Itsitamine",
+  "sigh": "Ohkamine",
+  "crying": "Nutmine",
+  "singing": "Laulmine",
+  "choir": "Koorilaulmine",
+  "yodeling": "Joodeldamine",
+  "chant": "Skandeerimine",
+  "mantra": "Mantra lugemine",
+  "child_singing": "Lastelaul",
+  "whistling": "Vilistamine",
+  "gasp": "Hingeldamine",
+  "pant": "Ähkimine",
+  "door": "Uks",
+  "mouse": "Hiir",
+  "keyboard": "Klahvistik",
+  "sink": "Kraanikauss",
+  "blender": "Kannmikser",
+  "clock": "Kell",
+  "scissors": "Käärid",
+  "hair_dryer": "Föön",
+  "toothbrush": "Hambahari",
+  "vehicle": "Sõiduk",
+  "bark": "Puukoor",
+  "goat": "Kits"
 }

web/public/locales/et/common.json

@@ -39,21 +39,144 @@
     "minute_other": "{{time}} minutit",
     "s": "{{time}} sek",
     "second_one": "{{time}} sekund",
-    "second_other": "{{time}} sekundit"
+    "second_other": "{{time}} sekundit",
+    "formattedTimestampHourMinute": {
+      "24hour": "HH:mm",
+      "12hour": "hh:mm aaa"
+    },
+    "formattedTimestampHourMinuteSecond": {
+      "24hour": "HH:mm:ss",
+      "12hour": "hh:mm:ss aaa"
+    },
+    "formattedTimestampFilename": {
+      "12hour": "yy-MM-dd-hh-mm-ss-a",
+      "24hour": "yy-MM-dd-HH-mm-ss"
+    },
+    "formattedTimestamp": {
+      "12hour": "MMM d, hh:mm:ss aaa",
+      "24hour": "MMM d, HH:mm:ss"
+    },
+    "formattedTimestamp2": {
+      "12hour": "dd.MM hh:mm:ssa",
+      "24hour": "d MMM HH:mm:ss"
+    },
+    "formattedTimestampMonthDayHourMinute": {
+      "12hour": "MMM d, hh:mm aaa",
+      "24hour": "MMM d, HH:mm"
+    },
+    "formattedTimestampMonthDayYear": {
+      "12hour": "MMM d, yyyy",
+      "24hour": "MMM d, yyyy"
+    },
+    "inProgress": "Töös",
+    "invalidStartTime": "Vigane algusaeg",
+    "invalidEndTime": "Vigane lõpuaeg",
+    "formattedTimestampMonthDayYearHourMinute": {
+      "12hour": "dd. MMM yyyy, hh:mm aaa",
+      "24hour": "dd. MMM yyyy, HH:mm"
+    },
+    "formattedTimestampMonthDay": "dd. MMM"
   },
   "menu": {
     "user": {
-      "setPassword": "Lisa salasõna"
+      "setPassword": "Lisa salasõna",
+      "logout": "Logi välja",
+      "title": "Kasutaja",
+      "account": "Kasutajakonto",
+      "current": "Praegune kasutaja: {{user}}",
+      "anonymous": "anonüümne"
     },
     "live": {
-      "allCameras": "Kõik kaamerad"
+      "allCameras": "Kõik kaamerad",
+      "title": "Otseülekanne",
+      "cameras": {
+        "title": "Kaamerad",
+        "count_one": "{{count}} kaamera",
+        "count_other": "{{count}} kaamerat"
+      }
     },
     "settings": "Seadistused",
     "language": {
       "withSystem": {
         "label": "Kasuta keele jaoks süsteemi seadistusi"
+      },
+      "en": "English (inglise keel)",
+      "es": "Español (hispaania keel)",
+      "zhCN": "简体中文 (hiina keel lihtsustatud hieroglüüfidega)",
+      "hi": "हिन्दी (hindi keel)",
+      "fr": "Français (prantsuse keel)",
+      "ar": "العربية (araabia keel)",
+      "pt": "Português (portugali keel)",
+      "ptBR": "Português brasileiro (Brasiilia portugali keel)",
+      "ru": "Русский (vene keel)",
+      "de": "Deutsch (saksa keel)",
+      "ja": "日本語 (jaapani keel)",
+      "tr": "Türkçe (türgi keel)",
+      "it": "Italiano (itaalia keel)",
+      "nl": "Nederlands (hollandi keel)",
+      "sv": "Svenska (rootsi keel)",
+      "cs": "Čeština (tšehhi keel)",
+      "nb": "Norsk Bokmål (norra bokmål)",
+      "ko": "한국어 (korea keel)",
+      "vi": "Tiếng Việt (vietnami keel)",
+      "fa": "فارسی (pärsia keel)",
+      "pl": "Polski (poola keel)",
+      "uk": "Українська (ukraina keel)",
+      "he": "עברית (heebrea keel)",
+      "el": "Ελληνικά (kreeka keel)",
+      "ro": "Română (rumeenia keel)",
+      "hu": "Magyar (ungari keel)",
+      "fi": "Suomi (soome keel)",
+      "da": "Dansk (taani keel)",
+      "sk": "Slovenčina (slovaki keel)",
+      "yue": "粵語 (kantoni keel)",
+      "th": "ไทย (tai keel)",
+      "ca": "Català (katalaani keel)",
+      "sr": "Српски (serbia keel)",
+      "sl": "Slovenščina (sloveeni keel)",
+      "lt": "Lietuvių (leedu keel)",
+      "bg": "Български (bulgaaria keel)",
+      "gl": "Galego (galeegi keel)",
+      "id": "Bahasa Indonesia (indoneesia keel)",
+      "ur": "اردو (urdu keel)"
+    },
+    "system": "Süsteem",
+    "systemMetrics": "Süsteemi meetrika",
+    "configuration": "Seadistused",
+    "systemLogs": "Süsteemi logid",
+    "configurationEditor": "Seadistuste haldur",
+    "languages": "Keeled",
+    "appearance": "Välimus",
+    "darkMode": {
+      "label": "Tume kujundus",
+      "light": "Hele kujundus",
+      "dark": "Tume kujundus",
+      "withSystem": {
+        "label": "Kasuta süsteemi seadistusi heleda või tumeda kujunduse jaoks"
       }
-    }
+    },
+    "withSystem": "Süsteem",
+    "theme": {
+      "label": "Kujundus",
+      "blue": "Sinine",
+      "green": "Roheline",
+      "nord": "Põhjala",
+      "red": "Punane",
+      "highcontrast": "Väga kontrastne",
+      "default": "Vaikimisi kujundus"
+    },
+    "help": "Abiteave",
+    "documentation": {
+      "title": "Dokumentatsioon",
+      "label": "Frigate'i dokumentatsioon"
+    },
+    "restart": "Käivita Frigate uuesti",
+    "review": "Ülevaatamine",
+    "explore": "Uuri",
+    "export": "Ekspordi",
+    "uiPlayground": "Leht kasutajaliidese katsetamiseks",
+    "faceLibrary": "Näoteek",
+    "classification": "Klassifikatsioon"
   },
   "unit": {
     "speed": {
@@ -67,6 +190,10 @@
       "kbph": "kB/t",
       "mbph": "MB/t",
       "gbph": "GB/t"
+    },
+    "length": {
+      "feet": "jalga",
+      "meters": "meetrit"
     }
   },
   "button": {
@@ -97,7 +224,15 @@
     "yes": "Jah",
     "no": "Ei",
     "download": "Laadi alla",
-    "info": "Teave"
+    "info": "Teave",
+    "suspended": "Peata",
+    "unsuspended": "Lõpeta peatamine",
+    "play": "Esita",
+    "unselect": "Eemalda valik",
+    "export": "Ekspordi",
+    "deleteNow": "Kustuta kohe",
+    "next": "Järgmine",
+    "continue": "Jätka"
   },
   "label": {
     "back": "Mine tagasi",
@@ -113,6 +248,50 @@
     "separatorWithSpace": ", "
   },
   "field": {
-    "optional": "Valikuline"
+    "optional": "Valikuline",
+    "internalID": "Seadistustes ja andmebaasis kasutatav Frigate'i sisemine tunnus"
+  },
+  "toast": {
+    "copyUrlToClipboard": "Võrguaadress on kopeeritud lõikelauale.",
+    "save": {
+      "title": "Salvesta",
+      "error": {
+        "title": "Seadistuste muudatuste salvestamine ei õnnestunud: {{errorMessage}}",
+        "noMessage": "Seadistuste muudatuste salvestamine ei õnnestunud"
+      }
+    }
+  },
+  "role": {
+    "title": "Roll",
+    "admin": "Peakasutaja",
+    "viewer": "Vaataja",
+    "desc": "Peakasutajatel on Frigate'i kasutajaliideses kõik õigused. Vaatajad võivad vaid kaamerate pilti vaadata, objekte ülevaadata ning otsida arhiivist vanu videoid."
+  },
+  "pagination": {
+    "label": "lehenummerdus",
+    "previous": {
+      "title": "Eelmine",
+      "label": "Mine eelmisele lehele"
+    },
+    "next": {
+      "title": "Järgmine",
+      "label": "Mine järgmisele lehele"
+    },
+    "more": "Järgnevad lehed"
+  },
+  "accessDenied": {
+    "documentTitle": "Ligipääs on keelatud - Frigate",
+    "title": "Ligipääs on keelatud",
+    "desc": "Sul pole õigusi selle lehe vaatamiseks."
+  },
+  "notFound": {
+    "documentTitle": "Lehte ei leidu - Frigate",
+    "title": "404",
+    "desc": "Veebilehte ei leidu"
+  },
+  "selectItem": "Vali {{item}}",
+  "readTheDocumentation": "Loe dokumentatsiooni ja juhendit",
+  "information": {
+    "pixels": "{{area}} px"
   }
 }

web/public/locales/et/components/camera.json

@@ -9,9 +9,39 @@
         "placeholder": "Vali videovoog",
         "streamMethod": {
           "label": "Voogedastuse meetod",
-          "placeholder": "Vali voogedastuse meetod"
+          "placeholder": "Vali voogedastuse meetod",
+          "method": {
+            "noStreaming": {
+              "label": "Voogedastust pole",
+              "desc": "Kaamerapildid uuenevad kord minutis ja voogedastust pole."
+            },
+            "smartStreaming": {
+              "label": "Nutikas voogedastus (soovituslik)",
+              "desc": "Nutika voogedastuse puhul ilma igasuguse tuvastatava tegevuseta kaamerapildid uuenevad kord minutis ja voogedastust pole. Sellega säästad ribalaiud ja kuid ressursse. Tegevuse tuvastamisel käivitub tavapärane voogedastus."
+            },
+            "continuousStreaming": {
+              "label": "Pidev voogedastus",
+              "desc": {
+                "title": "Kaamera voogedastus toimub töölauavaates pidevalt, seda ka siis, kui seal pole mingit tegevust tuvastatud.",
+                "warning": "Pidev voogedastus võib põhjustada suurt andmeedastuse mahutu ja tekitada jõudlusprobleeme. Kasuta seda võimalust ettevaatlikult."
+              }
+            }
+          }
+        },
+        "audioIsAvailable": "Selle voogedastuse puhul on saadaval ka heliriba",
+        "audioIsUnavailable": "Selle voogedastuse puhul pole heliriba saadaval",
+        "compatibilityMode": {
+          "label": "Ühilduvusrežiim",
+          "desc": "Kasuta seda võimalust vaid olukorras, kus kaamera voogedastuses paistab visuaalseid vigu ja pidi paremas ääres on diagonaalne joon."
+        },
+        "desc": "Muuda selle kaamergrupi voogedastuse valikuid töölauavaates.<em>Need seadistused on seadme- ja veebibrauserikohased.</em>",
+        "audio": {
+          "tips": {
+            "title": "See kaamera peab oskama heli jäädvustada ja edastada ja go2rtc kontekstis seadistatud selle voogedastuse jaoks."
+          }
         }
-      }
+      },
+      "birdseye": "Vaade linnulennult"
     },
     "add": "Lisa kaameragrupp",
     "edit": "Muuda kaameragruppi",
@@ -48,6 +78,9 @@
     },
     "boundingBox": "Piirdekast",
     "timestamp": "Ajatempel",
-    "zones": "Tsoonid"
+    "zones": "Tsoonid",
+    "mask": "Mask",
+    "motion": "Liikumine",
+    "regions": "Alad"
   }
 }

web/public/locales/et/components/dialog.json

@@ -1,6 +1,122 @@
 {
   "restart": {
     "title": "Kas oled kindel, et soovid Frigate'i uuesti käivitada?",
-    "button": "Käivita uuesti"
+    "button": "Käivita uuesti",
+    "restarting": {
+      "title": "Frigate käivitub uuesti",
+      "content": "See leht laaditakse uuesti {{countdown}} sekundi pärast.",
+      "button": "Laadi uuesti kohe"
+    }
+  },
+  "search": {
+    "saveSearch": {
+      "label": "Salvesta otsing",
+      "desc": "Sisesta nimi salvestatud otsingu jaoks.",
+      "placeholder": "Sisesta nimi oma otsingu jaoks",
+      "overwrite": "„{{searchName}}“ on juba olemas. Salvestamisel kirjutad olemasoleva väärtuse üle.",
+      "success": "„{{searchName}}“ otsing on salvestatud.",
+      "button": {
+        "save": {
+          "label": "Salvesta see otsing"
+        }
+      }
+    }
+  },
+  "explore": {
+    "video": {
+      "viewInHistory": "Vaata ajaloos"
+    },
+    "plus": {
+      "review": {
+        "state": {
+          "submitted": "Saadetud"
+        },
+        "question": {
+          "ask_a": "Kas see objekt on <code>{{label}}</code>?",
+          "ask_an": "Kas see objekt on <code>{{label}}</code>?",
+          "ask_full": "Kas see objekt on <code>{{untranslatedLabel}}</code> ({{translatedLabel}})?",
+          "label": "Kinnita see silt Frigate+ teenuse jaoks"
+        }
+      },
+      "submitToPlus": {
+        "label": "Saada teenusesse Frigate+",
+        "desc": "Objektid asukohtades, mida sa tahad vältida, pole valepositiivsed. Kui sa neid sellistena saadad teenusele, siis see ainult ajab tehisaru mudeli sassi."
+      }
+    }
+  },
+  "export": {
+    "time": {
+      "fromTimeline": "Vali ajajoonelt",
+      "lastHour_one": "Viimase tunni jooksul",
+      "lastHour_other": "Viimase {{count}} tunni jooksul",
+      "custom": "Sinu valitud ajavahemik",
+      "start": {
+        "title": "Algusaeg",
+        "label": "Vali algusaeg"
+      },
+      "end": {
+        "title": "Lõpuaeg",
+        "label": "Vali lõpuaeg"
+      }
+    },
+    "name": {
+      "placeholder": "Sisesta ekspordifaili nimi"
+    },
+    "select": "Vali",
+    "export": "Ekspordi",
+    "selectOrExport": "Vali või ekspordi",
+    "toast": {
+      "success": "Eksportimise käivitamine õnnestus. Faili leiad eksportimise lehelt.",
+      "view": "Vaata",
+      "error": {
+        "failed": "Eksportimise käivitamine ei õnnestunud: {{error}}",
+        "endTimeMustAfterStartTime": "Ajavahemiku lõpp peab olema peale algust",
+        "noVaildTimeSelected": "Ühtegi kehtivat ajavahemikku pole valitud"
+      }
+    },
+    "fromTimeline": {
+      "saveExport": "Salvesta eksporditud sisu",
+      "previewExport": "Eksporditud sisu eelvaade"
+    }
+  },
+  "streaming": {
+    "label": "Voogedastus",
+    "restreaming": {
+      "disabled": "Voogedastuse kordus pole selle kaamera puhul kasutatav.",
+      "desc": {
+        "title": "Kui tahad selle kaameraga kasutada täiendavaid otseeetri ja helivõimalusi, siis seadista go2rtc."
+      }
+    },
+    "debugView": "Veaotsinguvaade",
+    "showStats": {
+      "label": "Näita voogedastuse statistikat",
+      "desc": "Lülita see eelistus sisse, kui soovid kaamerapildi ülekattena näha voogedastuse statistikat."
+    }
+  },
+  "recording": {
+    "button": {
+      "export": "Ekspordi",
+      "markAsReviewed": "Märgi ülevaadatuks",
+      "markAsUnreviewed": "Märgi mitteülevaadatuks",
+      "deleteNow": "Kustuta kohe"
+    },
+    "confirmDelete": {
+      "title": "Kinnita kustutamine",
+      "desc": {
+        "selected": "Kas sa oled kindel et soovid selle kõik ülevaadatava objektiga seotud kirjed kustutada?<br /><br />Vajuta alla <em>Shift</em> klahv ja saad sellest vaatest tulevikus mööda minna."
+      },
+      "toast": {
+        "success": "Selle ülevaadatava objektiga seotud videosisu on kustutatud.",
+        "error": "Kustutamine ei õnnestunud: {{error}}"
+      }
+    }
+  },
+  "imagePicker": {
+    "selectImage": "Vali jälgitava objekti pisipilt",
+    "unknownLabel": "Päästikpilt on salvestatud",
+    "search": {
+      "placeholder": "Otsi sildi või alamsildi alusel..."
+    },
+    "noImages": "Selle kaamera kohta ei leidu pisipilte"
   }
 }

web/public/locales/et/components/filter.json

@@ -16,16 +16,21 @@
     "all": {
       "title": "Kõik sildid",
       "short": "Sildid"
-    }
+    },
+    "label": "Sildid",
+    "count_one": "{{count}} silt",
+    "count_other": "{{count}} silti"
   },
   "subLabels": {
-    "all": "Kõik alamsildid"
+    "all": "Kõik alamsildid",
+    "label": "Alamsildid"
   },
   "dates": {
     "all": {
       "title": "Kõik kuupäevad",
       "short": "Kuupäevad"
-    }
+    },
+    "selectPreset": "Vali eelseadistus…"
   },
   "explore": {
     "settings": {
@@ -53,5 +58,31 @@
     },
     "disableLogStreaming": "Keela logi voogedastus",
     "allLogs": "Kõik logid"
+  },
+  "classes": {
+    "label": "Klassid",
+    "all": {
+      "title": "Kõik klassid"
+    },
+    "count_one": "{{count}} klass",
+    "count_other": "{{count}} klassi"
+  },
+  "zones": {
+    "label": "Tsoonid",
+    "all": {
+      "title": "Kõik tsoonid",
+      "short": "Tsoonid"
+    }
+  },
+  "more": "Täiendavad filtrid",
+  "timeRange": "Ajavahemik",
+  "reset": {
+    "label": "Lähtesta filtrid vaikimisi väärtusteks"
+  },
+  "score": "Punktiskoor",
+  "estimatedSpeed": "Hinnanguline kiirus: ({{unit}})",
+  "features": {
+    "label": "Omadused",
+    "hasSnapshot": "Leidub hetkvõte"
   }
 }

web/public/locales/et/components/player.json

@@ -1,3 +1,8 @@
 {
-  "noRecordingsFoundForThisTime": "Hetkel ei leidu ühtego salvestust"
+  "noRecordingsFoundForThisTime": "Hetkel ei leidu ühtego salvestust",
+  "noPreviewFound": "Eelvaadet ei leidu",
+  "noPreviewFoundFor": "{{cameraName}} kaamera eelvaadet ei leidu",
+  "submitFrigatePlus": {
+    "submit": "Saada"
+  }
 }