Settings i18n improvements (#22571)

* i18n improvements for settings UI

- deduplicate shared detector translation keys and centralize config translation resolution
- add missing i18n keys

* formatting

generate_config_translations.py

@@ -190,20 +190,24 @@ def generate_section_translation(config_class: type) -> Dict[str, Any]:
 
 def get_detector_translations(
     config_schema: Dict[str, Any],
-) -> tuple[Dict[str, Any], set[str]]:
+) -> tuple[Dict[str, Any], Dict[str, Any], set[str]]:
-    """Build detector type translations with nested fields based on schema definitions."""
+    """Build detector type translations with nested fields based on schema definitions.
+
+    Returns a tuple of (type_translations, shared_fields, nested_field_keys).
+    Shared fields (identical across all detector types) are returned separately
+    to avoid duplication in the output.
+    """
     defs = config_schema.get("$defs", {})
     detector_schema = defs.get("DetectorConfig", {})
     discriminator = detector_schema.get("discriminator", {})
     mapping = discriminator.get("mapping", {})
 
-    type_translations: Dict[str, Any] = {}
+    # First pass: collect all nested fields per detector type
-    nested_field_keys: set[str] = set()
+    all_nested: Dict[str, Dict[str, Any]] = {}
-    for detector_type, ref in mapping.items():
+    type_meta: Dict[str, Dict[str, str]] = {}
-        if not isinstance(ref, str):
-            continue
 
-        if not ref.startswith("#/$defs/"):
+    for detector_type, ref in mapping.items():
+        if not isinstance(ref, str) or not ref.startswith("#/$defs/"):
             continue
 
         ref_name = ref.split("/")[-1]
@@ -211,26 +215,49 @@ def get_detector_translations(
         if not ref_schema:
             continue
 
-        type_entry: Dict[str, str] = {}
+        meta: Dict[str, str] = {}
         title = ref_schema.get("title")
         description = ref_schema.get("description")
         if title:
-            type_entry["label"] = title
+            meta["label"] = title
         if description:
-            type_entry["description"] = description
+            meta["description"] = description
+        type_meta[detector_type] = meta
 
         nested = extract_translations_from_schema(ref_schema, defs=defs)
-        nested_without_root = {
+        all_nested[detector_type] = {
             k: v for k, v in nested.items() if k not in ("label", "description")
         }
-        if nested_without_root:
+
-            type_entry.update(nested_without_root)
+    # Find fields that are identical across all types that have them
-            nested_field_keys.update(nested_without_root.keys())
+    shared_fields: Dict[str, Any] = {}
+    if all_nested:
+        # Collect all field keys across all types
+        all_keys: set[str] = set()
+        for nested in all_nested.values():
+            all_keys.update(nested.keys())
+
+        for key in all_keys:
+            values = [nested[key] for nested in all_nested.values() if key in nested]
+            if len(values) == len(all_nested) and all(v == values[0] for v in values):
+                shared_fields[key] = values[0]
+
+    # Build per-type translations with only unique (non-shared) fields
+    type_translations: Dict[str, Any] = {}
+    nested_field_keys: set[str] = set()
+    for detector_type, nested in all_nested.items():
+        type_entry: Dict[str, Any] = {}
+        type_entry.update(type_meta.get(detector_type, {}))
+
+        unique_fields = {k: v for k, v in nested.items() if k not in shared_fields}
+        if unique_fields:
+            type_entry.update(unique_fields)
+            nested_field_keys.update(unique_fields.keys())
 
         if type_entry:
             type_translations[detector_type] = type_entry
 
-    return type_translations, nested_field_keys
+    return type_translations, shared_fields, nested_field_keys
 
 
 def main():
@@ -303,9 +330,12 @@ def main():
             section_data.update(nested_without_root)
 
         if field_name == "detectors":
-            detector_types, detector_field_keys = get_detector_translations(
+            detector_types, shared_fields, detector_field_keys = (
-                config_schema
+                get_detector_translations(config_schema)
             )
+            # Add shared fields at the base detectors level
+            section_data.update(shared_fields)
+            # Add per-type translations (only unique fields per type)
             section_data.update(detector_types)
             for key in detector_field_keys:
                 if key == "type":

web/public/locales/en/config/global.json

@@ -286,13 +286,6 @@
   "detectors": {
     "label": "Detector hardware",
     "description": "Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
-    "type": {
-      "label": "Detector Type",
-      "description": "Type of detector to use for object detection (for example 'cpu', 'edgetpu', 'openvino')."
-    },
-    "axengine": {
-      "label": "AXEngine NPU",
-      "description": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime.",
     "type": {
       "label": "Type"
     },
@@ -343,62 +336,14 @@
     "model_path": {
       "label": "Detector specific model path",
       "description": "File path to the detector model binary if required by the chosen detector."
-      }
+    },
+    "axengine": {
+      "label": "AXEngine NPU",
+      "description": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime."
     },
     "cpu": {
       "label": "CPU",
       "description": "CPU TFLite detector that runs TensorFlow Lite models on the host CPU without hardware acceleration. Not recommended.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "num_threads": {
         "label": "Number of detection threads",
         "description": "The number of threads used for CPU-based inference."
@@ -407,57 +352,6 @@
     "deepstack": {
       "label": "DeepStack",
       "description": "DeepStack/CodeProject.AI detector that sends images to a remote DeepStack HTTP API for inference. Not recommended.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "api_url": {
         "label": "DeepStack API URL",
         "description": "The URL of the DeepStack API."
@@ -474,57 +368,6 @@
     "degirum": {
       "label": "DeGirum",
       "description": "DeGirum detector for running models via DeGirum cloud or local inference services.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "location": {
         "label": "Inference Location",
         "description": "Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1')."
@@ -541,57 +384,6 @@
     "edgetpu": {
       "label": "EdgeTPU",
       "description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "device": {
         "label": "Device Type",
         "description": "The device to use for EdgeTPU inference (e.g. 'usb', 'pci')."
@@ -600,57 +392,6 @@
     "hailo8l": {
       "label": "Hailo-8/Hailo-8L",
       "description": "Hailo-8/Hailo-8L detector using HEF models and the HailoRT SDK for inference on Hailo hardware.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "device": {
         "label": "Device Type",
         "description": "The device to use for Hailo inference (e.g. 'PCIe', 'M.2')."
@@ -659,57 +400,6 @@
     "memryx": {
       "label": "MemryX",
       "description": "MemryX MX3 detector that runs compiled DFP models on MemryX accelerators.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "device": {
         "label": "Device Path",
         "description": "The device to use for MemryX inference (e.g. 'PCIe')."
@@ -718,57 +408,6 @@
     "onnx": {
       "label": "ONNX",
       "description": "ONNX detector for running ONNX models; will use available acceleration backends (CUDA/ROCm/OpenVINO) when available.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "device": {
         "label": "Device Type",
         "description": "The device to use for ONNX inference (e.g. 'AUTO', 'CPU', 'GPU')."
@@ -777,57 +416,6 @@
     "openvino": {
       "label": "OpenVINO",
       "description": "OpenVINO detector for AMD and Intel CPUs, Intel GPUs and Intel VPU hardware.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "device": {
         "label": "Device Type",
         "description": "The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU')."
@@ -836,57 +424,6 @@
     "rknn": {
       "label": "RKNN",
       "description": "RKNN detector for Rockchip NPUs; runs compiled RKNN models on Rockchip hardware.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "num_cores": {
         "label": "Number of NPU cores to use.",
         "description": "The number of NPU cores to use (0 for auto)."
@@ -894,168 +431,15 @@
     },
     "synaptics": {
       "label": "Synaptics",
-      "description": "Synaptics NPU detector for models in .synap format using the Synap SDK on Synaptics hardware.",
+      "description": "Synaptics NPU detector for models in .synap format using the Synap SDK on Synaptics hardware."
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      }
     },
     "teflon_tfl": {
       "label": "Teflon",
-      "description": "Teflon delegate detector for TFLite using Mesa Teflon delegate library to accelerate inference on supported GPUs.",
+      "description": "Teflon delegate detector for TFLite using Mesa Teflon delegate library to accelerate inference on supported GPUs."
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      }
     },
     "tensorrt": {
       "label": "TensorRT",
       "description": "TensorRT detector for Nvidia Jetson devices using serialized TensorRT engines for accelerated inference.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "device": {
         "label": "GPU Device Index",
         "description": "The GPU device index to use."
@@ -1064,57 +448,6 @@
     "zmq": {
       "label": "ZMQ IPC",
       "description": "ZMQ IPC detector that offloads inference to an external process via a ZeroMQ IPC endpoint.",
-      "type": {
-        "label": "Type"
-      },
-      "model": {
-        "label": "Detector specific model configuration",
-        "description": "Detector-specific model configuration options (path, input size, etc.).",
-        "path": {
-          "label": "Custom Object detection model path",
-          "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
-        },
-        "labelmap_path": {
-          "label": "Label map for custom object detector",
-          "description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
-        },
-        "width": {
-          "label": "Object detection model input width",
-          "description": "Width of the model input tensor in pixels."
-        },
-        "height": {
-          "label": "Object detection model input height",
-          "description": "Height of the model input tensor in pixels."
-        },
-        "labelmap": {
-          "label": "Labelmap customization",
-          "description": "Overrides or remapping entries to merge into the standard labelmap."
-        },
-        "attributes_map": {
-          "label": "Map of object labels to their attribute labels",
-          "description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
-        },
-        "input_tensor": {
-          "label": "Model Input Tensor Shape",
-          "description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
-        },
-        "input_pixel_format": {
-          "label": "Model Input Pixel Color Format",
-          "description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
-        },
-        "input_dtype": {
-          "label": "Model Input D Type",
-          "description": "Data type of the model input tensor (for example 'float32')."
-        },
-        "model_type": {
-          "label": "Object Detection Model Type",
-          "description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
-        }
-      },
-      "model_path": {
-        "label": "Detector specific model path",
-        "description": "File path to the detector model binary if required by the chosen detector."
-      },
       "endpoint": {
         "label": "ZMQ IPC endpoint",
         "description": "The ZMQ endpoint to connect to."

web/public/locales/en/objects.json

@@ -116,5 +116,10 @@
   "nzpost": "NZPost",
   "postnord": "PostNord",
   "gls": "GLS",
-  "dpd": "DPD"
+  "dpd": "DPD",
+  "canada_post": "Canada Post",
+  "royal_mail": "Royal Mail",
+  "school_bus": "School Bus",
+  "skunk": "Skunk",
+  "kangaroo": "Kangaroo"
 }

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

@@ -92,6 +92,7 @@
     "triggers": "Triggers",
     "debug": "Debug",
     "frigateplus": "Frigate+",
+    "maintenance": "Maintenance",
     "mediaSync": "Media sync",
     "regionGrid": "Region grid"
   },

web/src/components/config-form/sectionExtras/CameraReviewStatusToggles.tsx

@@ -75,7 +75,9 @@ export default function CameraReviewStatusToggles({
             />
             <div className="space-y-0.5">
               <Label htmlFor="detections-enabled">
-                <Trans ns="views/settings">camera.review.detections</Trans>
+                <Trans ns="views/settings">
+                  cameraReview.review.detections
+                </Trans>
               </Label>
             </div>
           </div>

web/src/components/config-form/sections/BaseSection.tsx

@@ -1136,7 +1136,7 @@ export function ConfigSection({
                     )}
                   {hasChanges && (
                     <Badge variant="outline" className="text-xs">
-                      {t("modified", {
+                      {t("button.modified", {
                         ns: "common",
                         defaultValue: "Modified",
                       })}
@@ -1210,7 +1210,10 @@ export function ConfigSection({
                     variant="secondary"
                     className="cursor-default bg-danger text-xs text-white hover:bg-danger"
                   >
-                    {t("modified", { ns: "common", defaultValue: "Modified" })}
+                    {t("button.modified", {
+                      ns: "common",
+                      defaultValue: "Modified",
+                    })}
                   </Badge>
                 )}
               </div>

web/src/components/config-form/theme/templates/ErrorListTemplate.tsx

@@ -7,7 +7,11 @@ import type {
 import { Alert, AlertDescription, AlertTitle } from "@/components/ui/alert";
 import { LuCircleAlert } from "react-icons/lu";
 import { useTranslation } from "react-i18next";
-import { buildTranslationPath, humanizeKey } from "../utils";
+import {
+  buildTranslationPath,
+  resolveConfigTranslation,
+  humanizeKey,
+} from "../utils";
 import type { ConfigFormContext } from "@/types/configForm";
 
 type ErrorSchemaNode = RJSFSchema & {
@@ -114,22 +118,15 @@ const resolveErrorFieldLabel = ({
   );
 
   if (effectiveNamespace && translationPath) {
-    const prefixedTranslationKey =
+    const translated = resolveConfigTranslation(
-      sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
+      i18n,
-        ? `${sectionI18nPrefix}.${translationPath}.label`
+      t,
-        : undefined;
+      translationPath,
-    const translationKey = `${translationPath}.label`;
+      "label",
-
+      sectionI18nPrefix,
-    if (
+      effectiveNamespace,
-      prefixedTranslationKey &&
+    );
-      i18n.exists(prefixedTranslationKey, { ns: effectiveNamespace })
+    if (translated) return translated;
-    ) {
-      return t(prefixedTranslationKey, { ns: effectiveNamespace });
-    }
-
-    if (i18n.exists(translationKey, { ns: effectiveNamespace })) {
-      return t(translationKey, { ns: effectiveNamespace });
-    }
   }
 
   const schemaNode = resolveSchemaNodeForPath(schema, segments);

web/src/components/config-form/theme/templates/FieldTemplate.tsx

@@ -20,6 +20,7 @@ import { requiresRestartForFieldPath } from "@/utils/configUtil";
 import RestartRequiredIndicator from "@/components/indicators/RestartRequiredIndicator";
 import {
   buildTranslationPath,
+  resolveConfigTranslation,
   getFilterObjectLabel,
   hasOverrideAtPath,
   humanizeKey,
@@ -219,20 +220,16 @@ export function FieldTemplate(props: FieldTemplateProps) {
   // Try to get translated label, falling back to schema title, then RJSF label
   let finalLabel = label;
   if (effectiveNamespace && translationPath) {
-    // Prefer camera-scoped translations when a section prefix is provided
+    const translatedLabel = resolveConfigTranslation(
-    const prefixedTranslationKey =
+      i18n,
-      sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
+      t,
-        ? `${sectionI18nPrefix}.${translationPath}.label`
+      translationPath,
-        : undefined;
+      "label",
-    const translationKey = `${translationPath}.label`;
+      sectionI18nPrefix,
-
+      effectiveNamespace,
-    if (
+    );
-      prefixedTranslationKey &&
+    if (translatedLabel) {
-      i18n.exists(prefixedTranslationKey, { ns: effectiveNamespace })
+      finalLabel = translatedLabel;
-    ) {
-      finalLabel = t(prefixedTranslationKey, { ns: effectiveNamespace });
-    } else if (i18n.exists(translationKey, { ns: effectiveNamespace })) {
-      finalLabel = t(translationKey, { ns: effectiveNamespace });
     } else if (schemaTitle) {
       finalLabel = schemaTitle;
     } else if (translatedFilterObjectLabel) {
@@ -330,18 +327,16 @@ export function FieldTemplate(props: FieldTemplateProps) {
   // Try to get translated description, falling back to schema description
   let finalDescription = description || "";
   if (effectiveNamespace && translationPath) {
-    const prefixedDescriptionKey =
+    const translatedDescription = resolveConfigTranslation(
-      sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
+      i18n,
-        ? `${sectionI18nPrefix}.${translationPath}.description`
+      t,
-        : undefined;
+      translationPath,
-    const descriptionKey = `${translationPath}.description`;
+      "description",
-    if (
+      sectionI18nPrefix,
-      prefixedDescriptionKey &&
+      effectiveNamespace,
-      i18n.exists(prefixedDescriptionKey, { ns: effectiveNamespace })
+    );
-    ) {
+    if (translatedDescription) {
-      finalDescription = t(prefixedDescriptionKey, { ns: effectiveNamespace });
+      finalDescription = translatedDescription;
-    } else if (i18n.exists(descriptionKey, { ns: effectiveNamespace })) {
-      finalDescription = t(descriptionKey, { ns: effectiveNamespace });
     } else if (schemaDescription) {
       finalDescription = schemaDescription;
     }

web/src/components/config-form/theme/templates/ObjectFieldTemplate.tsx

@@ -17,6 +17,7 @@ import { requiresRestartForFieldPath } from "@/utils/configUtil";
 import { ConfigFormContext } from "@/types/configForm";
 import {
   buildTranslationPath,
+  resolveConfigTranslation,
   getDomainFromNamespace,
   getFilterObjectLabel,
   humanizeKey,
@@ -263,16 +264,14 @@ export function ObjectFieldTemplate(props: ObjectFieldTemplateProps) {
 
   let inferredLabel: string | undefined;
   if (i18nNs && translationPath) {
-    const prefixedLabelKey =
+    inferredLabel = resolveConfigTranslation(
-      sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
+      i18n,
-        ? `${sectionI18nPrefix}.${translationPath}.label`
+      t,
-        : undefined;
+      translationPath,
-    const labelKey = `${translationPath}.label`;
+      "label",
-    if (prefixedLabelKey && i18n.exists(prefixedLabelKey, { ns: i18nNs })) {
+      sectionI18nPrefix,
-      inferredLabel = t(prefixedLabelKey, { ns: i18nNs });
+      i18nNs,
-    } else if (i18n.exists(labelKey, { ns: i18nNs })) {
+    );
-      inferredLabel = t(labelKey, { ns: i18nNs });
-    }
   }
   if (!inferredLabel && translatedFilterLabel) {
     inferredLabel = translatedFilterLabel;
@@ -286,19 +285,14 @@ export function ObjectFieldTemplate(props: ObjectFieldTemplateProps) {
 
   let inferredDescription: string | undefined;
   if (i18nNs && translationPath) {
-    const prefixedDescriptionKey =
+    inferredDescription = resolveConfigTranslation(
-      sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
+      i18n,
-        ? `${sectionI18nPrefix}.${translationPath}.description`
+      t,
-        : undefined;
+      translationPath,
-    const descriptionKey = `${translationPath}.description`;
+      "description",
-    if (
+      sectionI18nPrefix,
-      prefixedDescriptionKey &&
+      i18nNs,
-      i18n.exists(prefixedDescriptionKey, { ns: i18nNs })
+    );
-    ) {
-      inferredDescription = t(prefixedDescriptionKey, { ns: i18nNs });
-    } else if (i18n.exists(descriptionKey, { ns: i18nNs })) {
-      inferredDescription = t(descriptionKey, { ns: i18nNs });
-    }
   }
   const schemaDescription = schema?.description;
   const fallbackDescription =

web/src/components/config-form/theme/utils/i18n.ts

@@ -124,6 +124,50 @@ export function buildTranslationPath(
   return stringSegments.join(".");
 }
 
+/**
+ * Resolve a translated label or description for a config form field.
+ *
+ * Tries keys in priority order:
+ *   1. Type-specific prefixed key (e.g. "detectors.edgetpu.device.label")
+ *   2. Shared prefixed key with type stripped (e.g. "detectors.device.label")
+ *   3. Unprefixed key (e.g. "device.label")
+ *
+ * @returns The translated string, or undefined if no key matched.
+ */
+export function resolveConfigTranslation(
+  i18n: { exists: (key: string, opts?: Record<string, unknown>) => boolean },
+  t: (key: string, opts?: Record<string, unknown>) => string,
+  translationPath: string,
+  suffix: "label" | "description",
+  sectionI18nPrefix?: string,
+  ns?: string,
+): string | undefined {
+  const opts = ns ? { ns } : undefined;
+
+  if (
+    sectionI18nPrefix &&
+    !translationPath.startsWith(`${sectionI18nPrefix}.`)
+  ) {
+    // 1. Type-specific prefixed key (e.g. detectors.edgetpu.device.label)
+    const prefixed = `${sectionI18nPrefix}.${translationPath}.${suffix}`;
+    if (i18n.exists(prefixed, opts)) return t(prefixed, opts);
+
+    // 2. Shared prefixed key — strip leading type segment
+    //    e.g. detectors.edgetpu.model.path → detectors.model.path
+    const dot = translationPath.indexOf(".");
+    if (dot !== -1) {
+      const shared = `${sectionI18nPrefix}.${translationPath.substring(dot + 1)}.${suffix}`;
+      if (i18n.exists(shared, opts)) return t(shared, opts);
+    }
+  }
+
+  // 3. Unprefixed key
+  const base = `${translationPath}.${suffix}`;
+  if (i18n.exists(base, opts)) return t(base, opts);
+
+  return undefined;
+}
+
 /**
  * Extract the filter object label from a path containing "filters" segment.
  * Returns the segment immediately after "filters".

web/src/components/config-form/theme/utils/index.ts

@@ -4,6 +4,7 @@
 
 export {
   buildTranslationPath,
+  resolveConfigTranslation,
   getFilterObjectLabel,
   humanizeKey,
   getDomainFromNamespace,

web/src/views/settings/SingleSectionPage.tsx

@@ -207,7 +207,10 @@ export function SingleSectionPage({
                 variant="secondary"
                 className="cursor-default bg-danger text-xs text-white hover:bg-danger"
               >
-                {t("modified", { ns: "common", defaultValue: "Modified" })}
+                {t("button.modified", {
+                  ns: "common",
+                  defaultValue: "Modified",
+                })}
               </Badge>
             )}
           </div>
@@ -242,7 +245,7 @@ export function SingleSectionPage({
               variant="secondary"
               className="cursor-default bg-danger text-xs text-white hover:bg-danger"
             >
-              {t("modified", { ns: "common", defaultValue: "Modified" })}
+              {t("button.modified", { ns: "common", defaultValue: "Modified" })}
             </Badge>
           )}
         </div>