Implement multi-model object detection

2026-07-26 21:59:02 +03:00 · 2026-07-23 17:51:21 -06:00
49 changed files with 1070 additions and 473 deletions
@@ -34,12 +34,13 @@ edgeTPU:
            type: edgetpu
            device: usb
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # <--- should match the imgsize of the model, typically 320
+            model_type: yolo-generic
-          height: 320 # <--- should match the imgsize of the model, typically 320
+            width: 320 # <--- should match the imgsize of the model, typically 320
-          path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
+            height: 320 # <--- should match the imgsize of the model, typically 320
-          labelmap_path: /config/labels-coco17.txt
+            path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
            labelmap_path: /config/labels-coco17.txt
 hailo8l:
  title: Hailo-8/Hailo-8L
  models:
@@ -67,27 +68,28 @@ hailo8l:
            type: hailo8l
            device: PCIe
-        model:
+        models:
-          width: 320
+          default:
-          height: 320
+            width: 320
-          input_tensor: nhwc
+            height: 320
-          input_pixel_format: rgb
+            input_tensor: nhwc
-          input_dtype: int
+            input_pixel_format: rgb
-          model_type: yolo-generic
+            input_dtype: int
-          labelmap_path: /labelmap/coco-80.txt
+            model_type: yolo-generic
            labelmap_path: /labelmap/coco-80.txt
-          # The detector automatically selects the default model based on your hardware:
+            # The detector automatically selects the default model based on your hardware:
-          # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
+            # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
-          # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
+            # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
-          #
+            #
-          # Optionally, you can specify a local model path to override the default.
+            # Optionally, you can specify a local model path to override the default.
-          # If a local path is provided and the file exists, it will be used instead of downloading.
+            # If a local path is provided and the file exists, it will be used instead of downloading.
-          # Example:
+            # Example:
-          # path: /config/model_cache/hailo/yolov6n.hef
+            # path: /config/model_cache/hailo/yolov6n.hef
-          #
+            #
-          # You can also override using a custom URL:
+            # You can also override using a custom URL:
-          # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
+            # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
-          # just make sure to give it the write configuration based on the model
+            # just make sure to give it the write configuration based on the model
    - key: ssd
      label: SSD MobileNet v1
      recommended: false
@@ -111,18 +113,19 @@ hailo8l:
            type: hailo8l
            device: PCIe
-        model:
+        models:
-          width: 300
+          default:
-          height: 300
+            width: 300
-          input_tensor: nhwc
+            height: 300
-          input_pixel_format: rgb
+            input_tensor: nhwc
-          model_type: ssd
+            input_pixel_format: rgb
-          # Specify the local model path (if available) or URL for SSD MobileNet v1.
+            model_type: ssd
-          # Example with a local path:
+            # Specify the local model path (if available) or URL for SSD MobileNet v1.
-          # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+            # Example with a local path:
-          #
+            # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
-          # Or override using a custom URL:
+            #
-          # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
+            # Or override using a custom URL:
            # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
 openvino:
  title: OpenVINO
  models:
@@ -171,14 +174,15 @@ openvino:
            type: openvino
            device: GPU # or NPU
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # <--- should match the imgsize set during model export
+            model_type: yolo-generic
-          height: 320 # <--- should match the imgsize set during model export
+            width: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
+            height: 320 # <--- should match the imgsize set during model export
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
    - key: ssd
      label: SSDLite MobileNet v2
      recommended: false
@@ -202,13 +206,14 @@ openvino:
            type: openvino
            device: GPU # Or NPU
-        model:
+        models:
-          width: 300
+          default:
-          height: 300
+            width: 300
-          input_tensor: nhwc
+            height: 300
-          input_pixel_format: bgr
+            input_tensor: nhwc
-          path: /openvino-model/ssdlite_mobilenet_v2.xml
+            input_pixel_format: bgr
-          labelmap_path: /openvino-model/coco_91cl_bkgr.txt
+            path: /openvino-model/ssdlite_mobilenet_v2.xml
            labelmap_path: /openvino-model/coco_91cl_bkgr.txt
    - key: yolo-legacy
      label: YOLO (v3, v4, v7)
      recommended: false
@@ -240,14 +245,15 @@ openvino:
            type: openvino
            device: GPU # or NPU
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # <--- should match the imgsize set during model export
+            model_type: yolo-generic
-          height: 320 # <--- should match the imgsize set during model export
+            width: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
+            height: 320 # <--- should match the imgsize set during model export
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
    - key: yolonas
      label: YOLO-NAS
      recommended: false
@@ -280,14 +286,15 @@ openvino:
            type: openvino
            device: GPU
-        model:
+        models:
-          model_type: yolonas
+          default:
-          width: 320 # <--- should match whatever was set in notebook
+            model_type: yolonas
-          height: 320 # <--- should match whatever was set in notebook
+            width: 320 # <--- should match whatever was set in notebook
-          input_tensor: nchw
+            height: 320 # <--- should match whatever was set in notebook
-          input_pixel_format: bgr
+            input_tensor: nchw
-          path: /config/yolo_nas_s.onnx
+            input_pixel_format: bgr
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/yolo_nas_s.onnx
            labelmap_path: /labelmap/coco-80.txt
    - key: yolox
      label: YOLOX
      recommended: false
@@ -308,10 +315,11 @@ openvino:
            type: openvino
            device: GPU
-        model:
+        models:
-          model_type: yolox
+          default:
-          path: /config/model_cache/yolox.onnx # use the filename you generated above
+            model_type: yolox
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolox.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
    - key: rfdetr
      label: RF-DETR
      recommended: false
@@ -350,13 +358,14 @@ openvino:
            type: openvino
            device: GPU
-        model:
+        models:
-          model_type: rfdetr
+          default:
-          width: 320
+            model_type: rfdetr
-          height: 320
+            width: 320
-          input_tensor: nchw
+            height: 320
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/rfdetr.onnx # use the filename you generated above
+            input_dtype: float
            path: /config/model_cache/rfdetr.onnx # use the filename you generated above
    - key: dfine
      label: D-FINE / DEIMv2
      recommended: false
@@ -448,14 +457,15 @@ openvino:
            type: openvino
            device: CPU
-        model:
+        models:
-          model_type: dfine
+          default:
-          width: 640
+            model_type: dfine
-          height: 640
+            width: 640
-          input_tensor: nchw
+            height: 640
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/dfine-s.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/dfine-s.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
 appleSilicon:
  title: Apple Silicon
  models:
@@ -504,14 +514,15 @@ appleSilicon:
            type: zmq
            endpoint: tcp://host.docker.internal:5555
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # <--- should match the imgsize set during model export
+            model_type: yolo-generic
-          height: 320 # <--- should match the imgsize set during model export
+            width: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
+            height: 320 # <--- should match the imgsize set during model export
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
    - key: yolo-legacy
      label: YOLO (v3, v4, v7)
      recommended: false
@@ -543,14 +554,15 @@ appleSilicon:
            type: zmq
            endpoint: tcp://host.docker.internal:5555
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # <--- should match the imgsize set during model export
+            model_type: yolo-generic
-          height: 320 # <--- should match the imgsize set during model export
+            width: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
+            height: 320 # <--- should match the imgsize set during model export
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
 onnx:
  title: ONNX
  models:
@@ -598,14 +610,15 @@ onnx:
          onnx:
            type: onnx
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # <--- should match the imgsize set during model export
+            model_type: yolo-generic
-          height: 320 # <--- should match the imgsize set during model export
+            width: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
+            height: 320 # <--- should match the imgsize set during model export
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
    - key: rfdetr
      label: RF-DETR
      recommended: false
@@ -643,13 +656,14 @@ onnx:
          onnx:
            type: onnx
-        model:
+        models:
-          model_type: rfdetr
+          default:
-          width: 320
+            model_type: rfdetr
-          height: 320
+            width: 320
-          input_tensor: nchw
+            height: 320
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/rfdetr.onnx # use the filename you generated above
+            input_dtype: float
            path: /config/model_cache/rfdetr.onnx # use the filename you generated above
    - key: yolonas
      label: YOLO-NAS
      recommended: false
@@ -681,14 +695,15 @@ onnx:
          onnx:
            type: onnx
-        model:
+        models:
-          model_type: yolonas
+          default:
-          width: 320 # <--- should match whatever was set in notebook
+            model_type: yolonas
-          height: 320 # <--- should match whatever was set in notebook
+            width: 320 # <--- should match whatever was set in notebook
-          input_pixel_format: bgr
+            height: 320 # <--- should match whatever was set in notebook
-          input_tensor: nchw
+            input_pixel_format: bgr
-          path: /config/yolo_nas_s.onnx
+            input_tensor: nchw
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/yolo_nas_s.onnx
            labelmap_path: /labelmap/coco-80.txt
    - key: yolox
      label: YOLOX
      recommended: false
@@ -711,14 +726,15 @@ onnx:
          onnx:
            type: onnx
-        model:
+        models:
-          model_type: yolox
+          default:
-          width: 416 # <--- should match the imgsize set during model export
+            model_type: yolox
-          height: 416 # <--- should match the imgsize set during model export
+            width: 416 # <--- should match the imgsize set during model export
-          input_tensor: nchw
+            height: 416 # <--- should match the imgsize set during model export
-          input_dtype: float_denorm
+            input_tensor: nchw
-          path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
+            input_dtype: float_denorm
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
    - key: dfine
      label: D-FINE / DEIMv2
      recommended: false
@@ -809,14 +825,15 @@ onnx:
          onnx:
            type: onnx
-        model:
+        models:
-          model_type: dfine
+          default:
-          width: 640
+            model_type: dfine
-          height: 640
+            width: 640
-          input_tensor: nchw
+            height: 640
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
    - key: yolo-legacy
      label: YOLO (v3, v4, v7)
      recommended: false
@@ -847,14 +864,15 @@ onnx:
          onnx:
            type: onnx
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # <--- should match the imgsize set during model export
+            model_type: yolo-generic
-          height: 320 # <--- should match the imgsize set during model export
+            width: 320 # <--- should match the imgsize set during model export
-          input_tensor: nchw
+            height: 320 # <--- should match the imgsize set during model export
-          input_dtype: float
+            input_tensor: nchw
-          path: /config/model_cache/yolo.onnx # use the filename you generated above
+            input_dtype: float
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/yolo.onnx # use the filename you generated above
            labelmap_path: /labelmap/coco-80.txt
 cpu:
  title: CPU
  models:
@@ -928,18 +946,19 @@ memryx:
            type: memryx
            device: PCIe:0
-        model:
+        models:
-          model_type: yolonas
+          default:
-          width: 320 # (Can be set to 640 for higher resolution)
+            model_type: yolonas
-          height: 320 # (Can be set to 640 for higher resolution)
+            width: 320 # (Can be set to 640 for higher resolution)
-          input_tensor: nchw
+            height: 320 # (Can be set to 640 for higher resolution)
-          input_dtype: float
+            input_tensor: nchw
-          labelmap_path: /labelmap/coco-80.txt
+            input_dtype: float
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            labelmap_path: /labelmap/coco-80.txt
-          # path: /config/yolonas.zip
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # The .zip file must contain:
+            # path: /config/yolonas.zip
-          # ├── yolonas.dfp          (a file ending with .dfp)
+            # The .zip file must contain:
-          # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
+            # ├── yolonas.dfp          (a file ending with .dfp)
            # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
    - key: yolov9
      label: YOLOv9
      recommended: false
@@ -965,17 +984,18 @@ memryx:
            type: memryx
            device: PCIe:0
-        model:
+        models:
-          model_type: yolo-generic
+          default:
-          width: 320 # (Can be set to 640 for higher resolution)
+            model_type: yolo-generic
-          height: 320 # (Can be set to 640 for higher resolution)
+            width: 320 # (Can be set to 640 for higher resolution)
-          input_tensor: nchw
+            height: 320 # (Can be set to 640 for higher resolution)
-          input_dtype: float
+            input_tensor: nchw
-          labelmap_path: /labelmap/coco-80.txt
+            input_dtype: float
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            labelmap_path: /labelmap/coco-80.txt
-          # path: /config/yolov9.zip
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # The .zip file must contain:
+            # path: /config/yolov9.zip
-          # ├── yolov9.dfp          (a file ending with .dfp)
+            # The .zip file must contain:
            # ├── yolov9.dfp          (a file ending with .dfp)
    - key: yolox
      label: YOLOX
      recommended: false
@@ -1001,17 +1021,18 @@ memryx:
            type: memryx
            device: PCIe:0
-        model:
+        models:
-          model_type: yolox
+          default:
-          width: 640
+            model_type: yolox
-          height: 640
+            width: 640
-          input_tensor: nchw
+            height: 640
-          input_dtype: float_denorm
+            input_tensor: nchw
-          labelmap_path: /labelmap/coco-80.txt
+            input_dtype: float_denorm
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            labelmap_path: /labelmap/coco-80.txt
-          # path: /config/yolox.zip
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # The .zip file must contain:
+            # path: /config/yolox.zip
-          # ├── yolox.dfp          (a file ending with .dfp)
+            # The .zip file must contain:
            # ├── yolox.dfp          (a file ending with .dfp)
    - key: ssd
      label: SSDLite MobileNet v2
      recommended: false
@@ -1037,18 +1058,19 @@ memryx:
            type: memryx
            device: PCIe:0
-        model:
+        models:
-          model_type: ssd
+          default:
-          width: 320
+            model_type: ssd
-          height: 320
+            width: 320
-          input_tensor: nchw
+            height: 320
-          input_dtype: float
+            input_tensor: nchw
-          labelmap_path: /labelmap/coco-80.txt
+            input_dtype: float
-          # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+            labelmap_path: /labelmap/coco-80.txt
-          # path: /config/ssdlite_mobilenet.zip
+            # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-          # The .zip file must contain:
+            # path: /config/ssdlite_mobilenet.zip
-          # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
+            # The .zip file must contain:
-          # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
+            # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
            # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
 tensorrt:
  title: TensorRT
  models:
@@ -1087,13 +1109,14 @@ tensorrt:
            type: tensorrt
            device: 0 #This is the default, select the first GPU
-        model:
+        models:
-          path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
+          default:
-          labelmap_path: /labelmap/coco-80.txt
+            path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
-          input_tensor: nchw
+            labelmap_path: /labelmap/coco-80.txt
-          input_pixel_format: rgb
+            input_tensor: nchw
-          width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
+            input_pixel_format: rgb
-          height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
+            width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
            height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
 synaptics:
  title: Synaptics
  models:
@@ -1261,14 +1284,15 @@ axengine:
          axengine:
            type: axengine
-        model:
+        models:
-          path: frigate-yolov9-tiny
+          default:
-          model_type: yolo-generic
+            path: frigate-yolov9-tiny
-          width: 320
+            model_type: yolo-generic
-          height: 320
+            width: 320
-          input_dtype: int
+            height: 320
-          input_pixel_format: bgr
+            input_dtype: int
-          labelmap_path: /labelmap/coco-80.txt
+            input_pixel_format: bgr
            labelmap_path: /labelmap/coco-80.txt
 degirumAiServer:
  title: DeGirum AI Server
  models:
@@ -157,44 +157,51 @@ auth:
      - front_door
      - back_yard
-# Optional: model modifications
+# Optional: named object detection models (default: a single model named "default")
 # Each entry defines a model; cameras choose which model to use with detect.model.
 # Detectors that support multiple models (openvino, onnx, tensorrt, cpu, rknn) run
 # one instance per model in use. Detectors that only support a single model
 # (edgetpu, hailo8l, memryx, and others) are assigned to models round robin, so at
 # least as many of those detectors as models are required when no multi-model
 # capable detector is configured.
 # NOTE: The default values are for the EdgeTPU detector.
 # Other detectors will require the model config to be set.
-model:
+models:
-  # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
+  default:
-  path: /edgetpu_model.tflite
+    # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
-  # Required: path to the labelmap (default: shown below)
+    path: /edgetpu_model.tflite
-  labelmap_path: /labelmap.txt
+    # Required: path to the labelmap (default: shown below)
-  # Required: Object detection model input width (default: shown below)
+    labelmap_path: /labelmap.txt
-  width: 320
+    # Required: Object detection model input width (default: shown below)
-  # Required: Object detection model input height (default: shown below)
+    width: 320
-  height: 320
+    # Required: Object detection model input height (default: shown below)
-  # Required: Object detection model input colorspace
+    height: 320
-  # Valid values are rgb, bgr, or yuv. (default: shown below)
+    # Required: Object detection model input colorspace
-  input_pixel_format: rgb
+    # Valid values are rgb, bgr, or yuv. (default: shown below)
-  # Required: Object detection model input tensor format
+    input_pixel_format: rgb
-  # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
+    # Required: Object detection model input tensor format
-  input_tensor: nhwc
+    # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
-  # Optional: Data type of the model input tensor
+    input_tensor: nhwc
-  # Valid values are float, float_denorm, or int (default: shown below)
+    # Optional: Data type of the model input tensor
-  input_dtype: int
+    # Valid values are float, float_denorm, or int (default: shown below)
-  # Required: Object detection model architecture, used by detectors that support more
+    input_dtype: int
-  # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
+    # Required: Object detection model architecture, used by detectors that support more
-  # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
+    # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
-  model_type: ssd
+    # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
-  # Required: Label name modifications. These are merged into the standard labelmap.
+    model_type: ssd
-  labelmap:
+    # Required: Label name modifications. These are merged into the standard labelmap.
-    2: vehicle
+    labelmap:
-  # Optional: Map of object labels to their attribute labels (default: depends on model)
+      2: vehicle
-  attributes_map:
+    # Optional: Map of object labels to their attribute labels (default: depends on model)
-    person:
+    attributes_map:
-      - amazon
+      person:
-      - face
+        - amazon
-    car:
+        - face
-      - amazon
+      car:
-      - fedex
+        - amazon
-      - license_plate
+        - fedex
-      - ups
+        - license_plate
        - ups
 # Optional: Audio Events Configuration
 # NOTE: Can be overridden at the camera level
@@ -302,6 +309,9 @@ ffmpeg:
 detect:
  # Optional: enables detection for the camera (default: shown below)
  enabled: False
  # Optional: name of the model (key under models) used by this camera
  # (default: the only defined model, or the model named "default")
  model: default
  # Optional: width of the frame for the input with the detect role (default: use native stream resolution)
  width: 1280
  # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
@@ -192,12 +192,13 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and open
 ```yaml
 # Optional: model config
-model:
+models:
-  path: /path/to/model
+  default:
-  width: 320
+    path: /path/to/model
-  height: 320
+    width: 320
-  input_tensor: "nhwc"
+    height: 320
-  input_pixel_format: "bgr"
+    input_tensor: "nhwc"
    input_pixel_format: "bgr"
 ```
 </TabItem>
@@ -214,15 +215,16 @@ If the labelmap is customized then the labels used for alerts will need to be ad
 The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
 ```yaml
-model:
+models:
-  labelmap:
+  default:
-    2: vehicle
+    labelmap:
-    3: vehicle
+      2: vehicle
-    5: vehicle
+      3: vehicle
-    7: vehicle
+      5: vehicle
-    15: animal
+      7: vehicle
-    16: animal
+      15: animal
-    17: animal
+      16: animal
      17: animal
 ```
 Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
@@ -334,13 +334,14 @@ detectors:
    type: openvino
    device: AUTO
-model:
+models:
-  width: 300
+  default:
-  height: 300
+    width: 300
-  input_tensor: nhwc
+    height: 300
-  input_pixel_format: bgr
+    input_tensor: nhwc
-  path: /openvino-model/ssdlite_mobilenet_v2.xml
+    input_pixel_format: bgr
-  labelmap_path: /openvino-model/coco_91cl_bkgr.txt
+    path: /openvino-model/ssdlite_mobilenet_v2.xml
    labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 record:
  enabled: True
@@ -91,6 +91,41 @@ The best detection accuracy comes from a model trained on images that look like
 :::
 ### Running multiple models
 Models are defined as named entries under `models`, and each camera selects the model it uses with `detect.model`. This makes it possible to run different models for different groups of cameras, for example a dedicated model for indoor cameras, outdoor cameras, or thermal cameras.
 ```yaml
 detectors:
  ov:
    type: openvino
    device: GPU
 models:
  indoor:
    path: /config/model_cache/indoor-model.xml
    model_type: yolo-generic
    width: 320
    height: 320
  outdoor:
    path: plus://<your_model_id>
 cameras:
  living_room:
    detect:
      model: indoor
  driveway:
    detect:
      model: outdoor
 ```
 When only one model is defined, all cameras use it automatically. With multiple models, cameras use the model named `default` unless `detect.model` selects another one; `detect.model` can also be set globally and overridden per camera.
 How detectors handle multiple models depends on the hardware:
 - **Detectors that support multiple models** (`openvino`, `onnx`, `tensorrt`, `cpu`, `rknn`): a single detector entry is automatically expanded into one instance per model in use. For example, detector `ov` with models `indoor` and `outdoor` runs as `ov_indoor` and `ov_outdoor`, and each instance appears separately in the System Metrics page. Keep in mind that each instance loads its own copy of the model, which increases GPU memory usage.
 - **Detectors that only support a single model** (`edgetpu`, `hailo8l`, `memryx`, and other single-session hardware): each detector entry serves exactly one model. Detector entries are assigned to models round robin, so running two models on Coral hardware requires two Corals. If these are the only detectors configured and there are fewer of them than models in use, Frigate will fail to start with an error explaining the options.
 # Officially Supported Detectors
 Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
@@ -789,11 +824,12 @@ You can set it to:
 - A path to some model.json.
 ```yaml
-model:
+models:
-  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
+  default:
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
+    path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 #### Local Inference
@@ -809,11 +845,12 @@ It is also possible to eliminate the need for an AI server and run the hardware
 Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
 ```yaml
-model:
+models:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+  default:
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
+    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 #### AI Hub Cloud Inference
@@ -829,11 +866,12 @@ If you do not possess whatever hardware you want to run, there's also the option
 Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
 ```yaml
-model:
+models:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
+  default:
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
+    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
+    width: 300 # width is in the model name as the first number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
+    height: 300 # height is in the model name as the second number in the "int"x"int" section
    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 ## AXERA
@@ -228,13 +228,14 @@ detectors: # <---- add detectors
    device: GPU
 # We will use the default MobileNet_v2 model from OpenVINO.
-model:
+models:
-  width: 300
+  default:
-  height: 300
+    width: 300
-  input_tensor: nhwc
+    height: 300
-  input_pixel_format: bgr
+    input_tensor: nhwc
-  path: /openvino-model/ssdlite_mobilenet_v2.xml
+    input_pixel_format: bgr
-  labelmap_path: /openvino-model/coco_91cl_bkgr.txt
+    path: /openvino-model/ssdlite_mobilenet_v2.xml
    labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 cameras:
  name_of_your_camera:
@@ -64,8 +64,9 @@ You can either choose the new model from the <NavPath path="Settings > System >
 ```yaml
 detectors: ...
-model:
+models:
-  path: plus://<your_model_id>
+  default:
    path: plus://<your_model_id>
 ```
 :::note
@@ -79,10 +80,11 @@ Models are downloaded into the `/config/model_cache` folder and only downloaded
 If needed, you can override the labelmap for Frigate+ models. This is not recommended as renaming labels will break the Submit to Frigate+ feature if the labels are not available in Frigate+.
 ```yaml
-model:
+models:
-  path: plus://<your_model_id>
+  default:
-  labelmap:
+    path: plus://<your_model_id>
-    3: animal
+    labelmap:
-    4: animal
+      3: animal
-    5: animal
+      4: animal
      5: animal
 ```
@@ -38,8 +38,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" />. In the *
 ```yaml
 detectors: ...
-model:
+models:
-  path: plus://<your_model_id>
+  default:
    path: plus://<your_model_id>
 ```
 :::tip
@@ -372,31 +372,40 @@ def config(request: Request):
        config["go2rtc"]["streams"][stream_name] = cleaned
    config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
    config["model"]["colormap"] = config_obj.model.colormap
    config["model"]["all_attributes"] = config_obj.model.all_attributes
    config["model"]["non_logo_attributes"] = config_obj.model.non_logo_attributes
-    # Add model plus data if plus is enabled
+    for model_key, model in config_obj.models.items():
-    if config["plus"]["enabled"]:
+        model_dict = config["models"][model_key]
-        model_path = config.get("model", {}).get("path")
+        model_dict["colormap"] = model.colormap
-        if model_path:
+        model_dict["all_attributes"] = model.all_attributes
-            model_json_path = FilePath(model_path).with_suffix(".json")
+        model_dict["non_logo_attributes"] = model.non_logo_attributes
            try:
                with open(model_json_path) as f:
                    model_plus_data = json.load(f)
                config["model"]["plus"] = model_plus_data
            except FileNotFoundError:
                config["model"]["plus"] = None
            except json.JSONDecodeError:
                config["model"]["plus"] = None
        else:
            config["model"]["plus"] = None
-    # use merged labelamp
+        # Add model plus data if plus is enabled
-    for detector_config in config["detectors"].values():
+        if config["plus"]["enabled"]:
-        detector_config["model"]["labelmap"] = (
+            model_plus_data = None
-            request.app.frigate_config.model.merged_labelmap
+
-        )
+            if model.path:
                model_json_path = FilePath(model.path).with_suffix(".json")
                try:
                    with open(model_json_path) as f:
                        model_plus_data = json.load(f)
                except (FileNotFoundError, json.JSONDecodeError):
                    model_plus_data = None
            model_dict["plus"] = model_plus_data
    # legacy single-model block kept for frontend compatibility, remove
    # once the UI is fully multi-model aware
    default_model_key = (
        "default" if "default" in config_obj.models else next(iter(config_obj.models))
    )
    config["model"] = config["models"][default_model_key]
    # use each detector's assigned merged labelmap
    for key, detector_config in config["detectors"].items():
        if config_obj.detectors[key].model:
            detector_config["model"]["labelmap"] = config_obj.detectors[
                key
            ].model.merged_labelmap
    return JSONResponse(content=config)
@@ -1323,8 +1332,11 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
    modelList = models["list"]
-    # current model type
+    # current model type, based on the default model until the UI is
-    modelType = request.app.frigate_config.model.model_type
+    # fully multi-model aware
    config_models = request.app.frigate_config.models
    default_model = config_models.get("default") or next(iter(config_models.values()))
    modelType = default_model.model_type
    # current detectorType for comparing to supportedDetectors
    detectorType = list(request.app.frigate_config.detectors.values())[0].type
@@ -813,7 +813,7 @@ async def event_snapshot(
            timestamp_style=request.app.frigate_config.cameras[
                event.camera
            ].timestamp_style,
-            colormap=request.app.frigate_config.model.colormap,
+            colormap=request.app.frigate_config.model_for_camera(event.camera).colormap,
        )
    except DoesNotExist:
        # see if the object is currently being tracked
@@ -97,7 +97,9 @@ class FrigateApp:
        self.metrics_manager = manager
        self.audio_process: mp.Process | None = None
        self.stop_event = stop_event
-        self.detection_queue: Queue = mp.Queue()
+        self.detection_queues: dict[str, Queue] = {
            model_key: mp.Queue() for model_key in config.models
        }
        self.detectors: dict[str, ObjectDetectProcess] = {}
        self.detection_shms: list[mp.shared_memory.SharedMemory] = []
        self.log_queue: Queue = mp.Queue()
@@ -363,20 +365,14 @@ class FrigateApp:
            )
    def start_detectors(self) -> None:
-        for name in self.config.cameras.keys():
+        for name, camera_config in self.config.cameras.items():
            camera_model = self.config.models[camera_config.detect.model]
            try:
                largest_frame = max(
                    [
                        det.model.height * det.model.width * 3
                        if det.model is not None
                        else 320
                        for det in self.config.detectors.values()
                    ]
                )
                shm_in = UntrackedSharedMemory(
                    name=name,
                    create=True,
-                    size=largest_frame,
+                    size=camera_model.height * camera_model.width * 3,
                )
            except FileExistsError:
                shm_in = UntrackedSharedMemory(name=name)
@@ -391,11 +387,16 @@ class FrigateApp:
            self.detection_shms.append(shm_in)
            self.detection_shms.append(shm_out)
-        for name, detector_config in self.config.detectors.items():
+        for name, detector_config in self.config.detector_instances.items():
            cameras_using_model = [
                camera_name
                for camera_name, camera_config in self.config.cameras.items()
                if camera_config.detect.model == detector_config.model_key
            ]
            self.detectors[name] = ObjectDetectProcess(
                name,
-                self.detection_queue,
+                self.detection_queues[detector_config.model_key],
-                list(self.config.cameras.keys()),
+                cameras_using_model,
                self.config,
                detector_config,
                self.stop_event,
@@ -430,7 +431,7 @@ class FrigateApp:
    def start_camera_processor(self) -> None:
        self.camera_maintainer = CameraMaintainer(
            self.config,
-            self.detection_queue,
+            self.detection_queues,
            self.detected_frames_queue,
            self.camera_metrics,
            self.ptz_metrics,
@@ -685,8 +686,9 @@ class FrigateApp:
        for detector in self.detectors.values():
            detector.stop()
-        empty_and_close_queue(self.detection_queue)
+        for detection_queue in self.detection_queues.values():
-        logger.info("Detection queue closed")
+            empty_and_close_queue(detection_queue)
        logger.info("Detection queues closed")
        self.detected_frames_processor.join()
        empty_and_close_queue(self.detected_frames_queue)
@@ -178,7 +178,7 @@ class CameraActivityManager:
            return
        for label in camera_config.objects.track:
-            if label in self.config.model.non_logo_attributes:
+            if label in self.config.model_for_camera(camera).non_logo_attributes:
                continue
            new_count = all_objects[label]
@@ -29,7 +29,7 @@ class CameraMaintainer(threading.Thread):
    def __init__(
        self,
        config: FrigateConfig,
-        detection_queue: Queue,
+        detection_queues: dict[str, Queue],
        detected_frames_queue: Queue,
        camera_metrics: DictProxy,
        ptz_metrics: dict[str, PTZMetrics],
@@ -38,7 +38,7 @@ class CameraMaintainer(threading.Thread):
    ):
        super().__init__(name="camera_processor")
        self.config = config
-        self.detection_queue = detection_queue
+        self.detection_queues = detection_queues
        self.detected_frames_queue = detected_frames_queue
        self.stop_event = stop_event
        self.camera_metrics = camera_metrics
@@ -79,10 +79,11 @@ class CameraMaintainer(threading.Thread):
        # create or update region grids for each camera
        for camera in self.config.cameras.values():
            assert camera.name is not None
            camera_model = self.config.models[camera.detect.model]
            self.region_grids[camera.name] = get_camera_regions_grid(
                camera.name,
                camera.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(camera_model.width, camera_model.height),
            )
    def __calculate_shm_frame_count(self) -> int:
@@ -115,6 +116,8 @@ class CameraMaintainer(threading.Thread):
        camera_stop_event = self.__ensure_camera_stop_event(name)
        camera_model = self.config.models[config.detect.model]
        if runtime:
            self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
            self.ptz_metrics[name] = PTZMetrics(
@@ -123,32 +126,24 @@ class CameraMaintainer(threading.Thread):
            self.region_grids[name] = get_camera_regions_grid(
                name,
                config.detect,
-                max(self.config.model.width, self.config.model.height),
+                max(camera_model.width, camera_model.height),
            )
            try:
                largest_frame = max(
                    [
                        det.model.height * det.model.width * 3
                        if det.model is not None
                        else 320
                        for det in self.config.detectors.values()
                    ]
                )
                UntrackedSharedMemory(name=f"out-{name}", create=True, size=20 * 6 * 4)
                UntrackedSharedMemory(
                    name=name,
                    create=True,
-                    size=largest_frame,
+                    size=camera_model.height * camera_model.width * 3,
                )
            except FileExistsError:
                pass
        camera_process = CameraTracker(
            config,
-            self.config.model,
+            camera_model,
-            self.config.model.merged_labelmap,
+            camera_model.merged_labelmap,
-            self.detection_queue,
+            self.detection_queues[config.detect.model],
            self.detected_frames_queue,
            self.camera_metrics[name],
            self.ptz_metrics[name],
@@ -40,6 +40,7 @@ class CameraState:
        self.name = name
        self.config = config
        self.camera_config = config.cameras[name]
        self.model_config = config.model_for_camera(name)
        self.frame_manager = frame_manager
        self.best_objects: dict[str, TrackedObject] = {}
        self.tracked_objects: dict[str, TrackedObject] = {}
@@ -101,7 +102,7 @@ class CameraState:
                        thickness = 1
                    else:
                        thickness = 2
-                        color = self.config.model.colormap.get(
+                        color = self.model_config.colormap.get(
                            obj["label"], (255, 255, 255)
                        )
                else:
@@ -125,7 +126,7 @@ class CameraState:
                    and obj["frame_time"] == frame_time
                ):
                    thickness = 5
-                    color = self.config.model.colormap.get(
+                    color = self.model_config.colormap.get(
                        obj["label"], (255, 255, 255)
                    )
@@ -261,7 +262,7 @@ class CameraState:
        if draw_options.get("paths"):
            for obj in tracked_objects.values():
                if obj["frame_time"] == frame_time and obj["path_data"]:
-                    color = self.config.model.colormap.get(
+                    color = self.model_config.colormap.get(
                        obj["label"], (255, 255, 255)
                    )
@@ -366,7 +367,7 @@ class CameraState:
        for id in new_ids:
            logger.debug(f"{self.name}: New tracked object ID: {id}")
            new_obj = tracked_objects[id] = TrackedObject(
-                self.config.model,
+                self.model_config,
                self.camera_config,
                self.config.ui,
                self.frame_cache,
@@ -510,7 +511,7 @@ class CameraState:
                sub_label = None
                if obj.obj_data.get("sub_label"):
-                    if obj.obj_data["sub_label"][0] in self.config.model.all_attributes:
+                    if obj.obj_data["sub_label"][0] in self.model_config.all_attributes:
                        label = obj.obj_data["sub_label"][0]
                    else:
                        label = f"{object_type}-verified"
@@ -156,10 +156,11 @@ class Dispatcher:
            if camera not in self.config.cameras:
                return None
            camera_model = self.config.model_for_camera(camera)
            grid = get_camera_regions_grid(
                camera,
                self.config.cameras[camera].detect,
-                max(self.config.model.width, self.config.model.height),
+                max(camera_model.width, camera_model.height),
            )
            return grid
@@ -50,6 +50,11 @@ class DetectConfig(FrigateBaseModel):
        title="Enable object detection",
        description="Enable or disable object detection for all cameras; can be overridden per-camera.",
    )
    model: str | None = Field(
        default=None,
        title="Detection model name",
        description="Name of the model (key under `models`) used by this camera. Defaults to the only defined model, or the model named 'default'.",
    )
    height: int | None = Field(
        default=None,
        title="Detect height",
@@ -4,6 +4,7 @@ import io
 import json
 import logging
 import os
 import re
 from typing import Any, Self
 import numpy as np
@@ -11,6 +12,7 @@ from pydantic import (
    BaseModel,
    ConfigDict,
    Field,
    PrivateAttr,
    TypeAdapter,
    ValidationInfo,
    field_validator,
@@ -19,7 +21,11 @@ from pydantic import (
 from ruamel.yaml import YAML
 from frigate.const import REGEX_JSON
-from frigate.detectors import DetectorConfig, ModelConfig
+from frigate.detectors import (
    DetectorConfig,
    ModelConfig,
    assign_detector_instances,
 )
 from frigate.detectors.detector_config import BaseDetectorConfig
 from frigate.plus import PlusApi
 from frigate.util.builtin import (
@@ -109,7 +115,7 @@ DEFAULT_CONFIG = f"""
 mqtt:
  enabled: False
-{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "model": DEFAULT_MODEL})}
+{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "models": {"default": DEFAULT_MODEL}})}
 cameras: {{}}  # No cameras defined, UI wizard should be used
 version: {CURRENT_CONFIG_VERSION}
 """
@@ -503,10 +509,10 @@ class FrigateConfig(FrigateBaseModel):
        title="Detector hardware",
        description="Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
    )
-    model: ModelConfig = Field(
+    models: dict[str, ModelConfig] = Field(
-        default_factory=ModelConfig,
+        default_factory=lambda: {"default": ModelConfig()},
-        title="Detection model",
+        title="Detection models",
-        description="Settings to configure a custom object detection model and its input shape.",
+        description="Named object detection models. Cameras select a model with detect.model; detectors are assigned to models automatically.",
    )
    # GenAI config (named provider configs: name -> GenAIConfig)
@@ -621,11 +627,37 @@ class FrigateConfig(FrigateBaseModel):
    )
    _plus_api: PlusApi
    _detector_instances: dict[str, BaseDetectorConfig] = PrivateAttr(
        default_factory=dict
    )
    @property
    def plus_api(self) -> PlusApi:
        return self._plus_api
    @property
    def detector_instances(self) -> dict[str, BaseDetectorConfig]:
        """Runtime detector instances expanded per assigned model."""
        return self._detector_instances
    def model_for_camera(self, camera_name: str) -> ModelConfig:
        """Return the detection model config used by the given camera."""
        return self.models[self.cameras[camera_name].detect.model]
    @field_validator("models")
    @classmethod
    def validate_model_names(cls, v: dict[str, ModelConfig]):
        if not v:
            raise ValueError("At least one model must be defined under models")
        for name in v.keys():
            if not re.match(r"^[a-zA-Z0-9_-]+$", name):
                raise ValueError(
                    f"Invalid model name '{name}'. Model names can only contain letters, numbers, underscores, and hyphens"
                )
        return v
    @model_validator(mode="after")
    def post_validation(self, info: ValidationInfo) -> Self:
        # Load plus api from context, if possible.
@@ -671,7 +703,12 @@ class FrigateConfig(FrigateBaseModel):
                )
        # set default min_score for object attributes
-        for attribute in self.model.all_attributes:
+        all_model_attributes = {
            attribute
            for model in self.models.values()
            for attribute in model.all_attributes
        }
        for attribute in sorted(all_model_attributes):
            existing = self.objects.filters.get(attribute)
            if existing is None:
                self.objects.filters[attribute] = FilterConfig(min_score=0.7)
@@ -721,8 +758,18 @@ class FrigateConfig(FrigateBaseModel):
            exclude_unset=True,
        )
        # capture raw model dumps before plus models are loaded so detector
        # instances can run their own detector-specific plus validation
        raw_model_dumps = {
            name: model.model_dump(exclude_unset=True, warnings="none")
            for name, model in self.models.items()
        }
        for model in self.models.values():
            model.check_and_load_plus_model(self.plus_api)
        adapter = TypeAdapter(DetectorConfig)
        for key, detector in self.detectors.items():
            adapter = TypeAdapter(DetectorConfig)
            model_dict = (
                detector
                if isinstance(detector, dict)
@@ -737,27 +784,6 @@ class FrigateConfig(FrigateBaseModel):
                )
                detector_config.model = None
            model_config = self.model.model_dump(exclude_unset=True, warnings="none")
            if detector_config.model_path:
                model_config["path"] = detector_config.model_path
            if "path" not in model_config:
                if detector_config.type == "cpu" or detector_config.type.endswith(
                    "_tfl"
                ):
                    model_config["path"] = "/cpu_model.tflite"
                elif detector_config.type == "edgetpu":
                    model_config["path"] = "/edgetpu_model.tflite"
                elif detector_config.type == "openvino":
                    for default_key, default_value in DEFAULT_MODEL.items():
                        model_config.setdefault(default_key, default_value)
            model = ModelConfig.model_validate(model_config)
            model.check_and_load_plus_model(self.plus_api, detector_config.type)
            model.compute_model_hash()
            labelmap_objects = model.merged_labelmap.values()
            detector_config.model = model
            self.detectors[key] = detector_config
        for name, camera in self.cameras.items():
@@ -785,6 +811,21 @@ class FrigateConfig(FrigateBaseModel):
                {"name": name, **merged_config}
            )
            # resolve which named model this camera uses
            if camera_config.detect.model is not None:
                if camera_config.detect.model not in self.models:
                    raise ValueError(
                        f"Camera {name} references model '{camera_config.detect.model}' which is not defined under models. Defined models: {', '.join(self.models.keys())}"
                    )
            elif len(self.models) == 1:
                camera_config.detect.model = next(iter(self.models))
            elif "default" in self.models:
                camera_config.detect.model = "default"
            else:
                raise ValueError(
                    f"Camera {name} does not specify detect.model and multiple models are defined. Set detect.model on the camera or globally, or name one of the models 'default'."
                )
            if camera_config.ffmpeg.hwaccel_args == "auto":
                camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
@@ -1005,7 +1046,10 @@ class FrigateConfig(FrigateBaseModel):
            verify_profile_overrides_match_base(camera_config)
            verify_autotrack_zones(camera_config)
            verify_motion_and_detect(camera_config)
-            verify_objects_track(camera_config, labelmap_objects)
+            verify_objects_track(
                camera_config,
                self.models[camera_config.detect.model].merged_labelmap.values(),
            )
            verify_lpr_and_face(self, camera_config)
        # Validate camera profiles reference top-level profile definitions
@@ -1022,8 +1066,11 @@ class FrigateConfig(FrigateBaseModel):
            config.name = name
        self.objects.parse_all_objects(self.cameras)
-        self.model.create_colormap(sorted(self.objects.all_objects))
+        for model in self.models.values():
-        self.model.check_and_load_plus_model(self.plus_api)
+            model.create_colormap(sorted(self.objects.all_objects))
        # expand detectors into per-model runtime instances
        self.__build_detector_instances(raw_model_dumps)
        # Check audio transcription and audio detection requirements
        if self.audio_transcription.enabled:
@@ -1054,6 +1101,81 @@ class FrigateConfig(FrigateBaseModel):
        return self
    def __build_detector_instances(
        self, raw_model_dumps: dict[str, dict[str, Any]]
    ) -> None:
        """Expand detector entries into runtime instances, one per assigned model."""
        used_models = list(
            dict.fromkeys(camera.detect.model for camera in self.cameras.values())
        ) or list(self.models.keys())
        unused_models = set(self.models.keys()) - set(used_models)
        if unused_models:
            logger.warning(
                f"Models {', '.join(sorted(unused_models))} are defined but not used by any camera, no detector instances will be created for them"
            )
        assignments = assign_detector_instances(
            {key: detector.type for key, detector in self.detectors.items()},
            used_models,
        )
        models_per_detector: dict[str, int] = {}
        for _, detector_key, _ in assignments:
            models_per_detector[detector_key] = (
                models_per_detector.get(detector_key, 0) + 1
            )
        instances: dict[str, BaseDetectorConfig] = {}
        for instance_name, detector_key, model_key in assignments:
            instance = self.detectors[detector_key].model_copy(deep=True)
            instance.model_key = model_key
            model_dict = raw_model_dumps[model_key].copy()
            if instance.model_path:
                if models_per_detector[detector_key] > 1:
                    logger.warning(
                        f"Detector {detector_key} runs multiple models, its model_path will be ignored"
                    )
                else:
                    model_dict["path"] = instance.model_path
            if "path" not in model_dict:
                if instance.type == "cpu" or instance.type.endswith("_tfl"):
                    model_dict["path"] = "/cpu_model.tflite"
                elif instance.type == "edgetpu":
                    model_dict["path"] = "/edgetpu_model.tflite"
                elif instance.type == "openvino":
                    for default_key, default_value in DEFAULT_MODEL.items():
                        model_dict.setdefault(default_key, default_value)
            model = ModelConfig.model_validate(model_dict)
            try:
                model.check_and_load_plus_model(self.plus_api, instance.type)
            except ValueError as e:
                raise ValueError(f"Model '{model_key}': {e}") from e
            model.compute_model_hash()
            instance.model = model
            instances[instance_name] = instance
            logger.log(
                logging.INFO if len(used_models) > 1 else logging.DEBUG,
                f"Detector instance {instance_name} ({instance.type}) will run model '{model_key}'",
            )
        # populate user-facing detector entries with their first assigned
        # model for display purposes
        for instance_name, detector_key, model_key in assignments:
            detector = self.detectors[detector_key]
            if detector.model is None:
                detector.model = instances[instance_name].model
                detector.model_key = model_key
        self._detector_instances = instances
    @field_validator("cameras")
    @classmethod
    def ensure_zones_and_cameras_have_different_names(cls, v: dict[str, CameraConfig]):
@@ -72,9 +72,10 @@ class LicensePlateProcessingMixin:
        # Object config
        self.lp_objects: list[str] = []
-        for obj, attributes in self.config.model.attributes_map.items():
+        for model in self.config.models.values():
-            if "license_plate" in attributes:
+            for obj, attributes in model.attributes_map.items():
-                self.lp_objects.append(obj)
+                if "license_plate" in attributes and obj not in self.lp_objects:
                    self.lp_objects.append(obj)
        # Detection specific parameters
        self.min_size = 8
@@ -231,8 +231,12 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                    final_data,
                    thumbs,
                    camera_config.review.genai,
-                    list(self.config.model.merged_labelmap.values()),
+                    list(
-                    self.config.model.all_attributes,
+                        self.config.model_for_camera(
                            camera_config.name
                        ).merged_labelmap.values()
                    ),
                    self.config.model_for_camera(camera_config.name).all_attributes,
                ),
            ).start()
@@ -1,11 +1,69 @@
 import logging
 from .detector_config import InputTensorEnum, ModelConfig, PixelFormatEnum  # noqa: F401
-from .detector_types import DetectorConfig, DetectorTypeEnum, api_types  # noqa: F401
+from .detector_types import (  # noqa: F401
    DetectorConfig,
    DetectorTypeEnum,
    api_types,
    detector_supports_multiple_models,
 )
 logger = logging.getLogger(__name__)
 def assign_detector_instances(
    detector_types: dict[str, str],
    used_models: list[str],
 ) -> list[tuple[str, str, str]]:
    """Assign detector entries to models.
    Detector types that support multiple models get one instance per model.
    Single-model detector entries are round-robin assigned across the models,
    wrapping around so that every detector entry is assigned.
    Args:
        detector_types: Detector key to detector type, in config order
        used_models: Ordered model keys in use by cameras
    Returns:
        List of (instance_name, detector_key, model_key) assignments
    """
    multi = [
        key
        for key, type_key in detector_types.items()
        if detector_supports_multiple_models(type_key)
    ]
    single = [key for key in detector_types if key not in multi]
    if not multi and len(single) < len(used_models):
        single_types = sorted({detector_types[key] for key in single})
        raise ValueError(
            f"Detectors {', '.join(single)} (types: {', '.join(single_types)}) can each only run a single model, "
            f"but {len(used_models)} models are in use ({', '.join(used_models)}). "
            "Add more detectors, use a detector type that supports multiple models, or reduce the number of models assigned to cameras."
        )
    assignments: list[tuple[str, str, str]] = []
    for key in multi:
        for model_key in used_models:
            instance_name = key if len(used_models) == 1 else f"{key}_{model_key}"
            assignments.append((instance_name, key, model_key))
    for i, key in enumerate(single):
        assignments.append((key, key, used_models[i % len(used_models)]))
    instance_names = [name for name, _, _ in assignments]
    duplicates = {name for name in instance_names if instance_names.count(name) > 1}
    if duplicates:
        raise ValueError(
            f"Detector instance names collide: {', '.join(sorted(duplicates))}. "
            "Rename the conflicting detectors or models so that expanded instance names (detector_model) are unique."
        )
    return assignments
 def create_detector(detector_config):
    if detector_config.type == DetectorTypeEnum.cpu:
        logger.warning(
@@ -11,6 +11,10 @@ logger = logging.getLogger(__name__)
 class DetectionApi(ABC):
    type_key: str
    supported_models: list[ModelTypeEnum]
    # whether this detector type can run multiple model instances concurrently
    # on the same hardware (one detector config entry can be expanded to an
    # instance per model); single-model detectors serve exactly one model each
    supports_multiple_models: bool = False
    @abstractmethod
    def __init__(self, detector_config: BaseDetectorConfig):
@@ -250,6 +250,11 @@ class BaseDetectorConfig(BaseModel):
        title="Detector specific model path",
        description="File path to the detector model binary if required by the chosen detector.",
    )
    model_key: str | None = Field(
        default=None,
        title="Assigned model name",
        description="Name of the model (key under `models`) this detector instance serves. Set automatically at runtime, users should not set this.",
    )
    model_config = ConfigDict(
        extra="allow", arbitrary_types_allowed=True, protected_namespaces=()
    )
@@ -29,6 +29,12 @@ for _, name, _ in _included_modules:
 api_types = {det.type_key: det for det in DetectionApi.__subclasses__()}
 def detector_supports_multiple_models(type_key: str) -> bool:
    """Return whether the given detector type can run multiple model instances."""
    detector = api_types.get(type_key)
    return bool(detector and getattr(detector, "supports_multiple_models", False))
 class StrEnum(str, Enum):
    pass
@@ -37,6 +37,7 @@ class CpuDetectorConfig(BaseDetectorConfig):
 class CpuTfl(DetectionApi):
    type_key = DETECTOR_KEY
    supports_multiple_models = True
    def __init__(self, detector_config: CpuDetectorConfig):
        # Suppress TFLite delegate creation messages that bypass Python logging
@@ -41,6 +41,7 @@ class ONNXDetectorConfig(BaseDetectorConfig):
 class ONNXDetector(DetectionApi):
    type_key = DETECTOR_KEY
    supports_multiple_models = True
    def __init__(self, detector_config: ONNXDetectorConfig):
        super().__init__(detector_config)
@@ -36,6 +36,7 @@ class OvDetectorConfig(BaseDetectorConfig):
 class OvDetector(DetectionApi):
    type_key = DETECTOR_KEY
    supports_multiple_models = True
    supported_models = [
        ModelTypeEnum.dfine,
        ModelTypeEnum.rfdetr,
@@ -47,6 +47,7 @@ class RknnDetectorConfig(BaseDetectorConfig):
 class Rknn(DetectionApi):
    type_key = DETECTOR_KEY
    supports_multiple_models = True
    def __init__(self, config: RknnDetectorConfig):
        super().__init__(config)
@@ -30,6 +30,7 @@ class TeflonDetectorConfig(BaseDetectorConfig):
 class TeflonTfl(DetectionApi):
    type_key = DETECTOR_KEY
    supports_multiple_models = True
    def __init__(self, detector_config: TeflonDetectorConfig):
        # Location in Debian's mesa-teflon-delegate
@@ -82,6 +82,7 @@ class HostDeviceMem:
 class TensorRtDetector(DetectionApi):
    type_key = DETECTOR_KEY
    supports_multiple_models = True
    def _load_engine(self, model_path):
        try:
@@ -159,7 +159,16 @@ class EventProcessor(threading.Thread):
            if width is None or height is None:
                return
-            first_detector = list(self.config.detectors.values())[0]
+            # find a detector instance running this camera's model so the
            # event records the model that produced it
            camera_detector = next(
                (
                    detector
                    for detector in self.config.detector_instances.values()
                    if detector.model_key == camera_config.detect.model
                ),
                list(self.config.detectors.values())[0],
            )
            start_time = event_data["start_time"]
            end_time = (
@@ -229,13 +238,13 @@ class EventProcessor(threading.Thread):
                Event.thumbnail: event_data.get("thumbnail"),
                Event.has_clip: event_data["has_clip"],
                Event.has_snapshot: event_data["has_snapshot"],
-                Event.model_hash: first_detector.model.model_hash
+                Event.model_hash: camera_detector.model.model_hash
-                if first_detector.model
+                if camera_detector.model
                else None,
-                Event.model_type: first_detector.model.model_type
+                Event.model_type: camera_detector.model.model_type
-                if first_detector.model
+                if camera_detector.model
                else None,
-                Event.detector_type: first_detector.type,
+                Event.detector_type: camera_detector.type,
                Event.data: {
                    "box": box,
                    "region": region,
@@ -436,7 +436,10 @@ class ReviewSegmentMaintainer(threading.Thread):
                if not object["sub_label"]:
                    segment.detections[object["id"]] = object["label"]
-                elif object["sub_label"][0] in self.config.model.all_attributes:
+                elif (
                    object["sub_label"][0]
                    in self.config.model_for_camera(segment.camera).all_attributes
                ):
                    segment.detections[object["id"]] = object["sub_label"][0]
                else:
                    segment.detections[object["id"]] = f"{object['label']}-verified"
@@ -574,7 +577,10 @@ class ReviewSegmentMaintainer(threading.Thread):
            for object in activity.get_all_objects():
                if not object["sub_label"]:
                    detections[object["id"]] = object["label"]
-                elif object["sub_label"][0] in self.config.model.all_attributes:
+                elif (
                    object["sub_label"][0]
                    in self.config.model_for_camera(camera).all_attributes
                ):
                    detections[object["id"]] = object["sub_label"][0]
                else:
                    detections[object["id"]] = f"{object['label']}-verified"
@@ -86,7 +86,7 @@ class TestConfig(unittest.TestCase):
                },
            },
            # needs to be a file that will exist, doesn't matter what
-            "model": {"path": "/etc/hosts", "width": 512},
+            "models": {"default": {"path": "/etc/hosts", "width": 512}},
        }
        frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
@@ -103,7 +103,7 @@ class TestConfig(unittest.TestCase):
        assert frigate_config.detectors["edgetpu"].device is None
        assert frigate_config.detectors["openvino"].device is None
-        assert frigate_config.model.path == "/etc/hosts"
+        assert frigate_config.models["default"].path == "/etc/hosts"
        assert frigate_config.detectors["cpu"].model.path == "/cpu_model.tflite"
        assert frigate_config.detectors["edgetpu"].model.path == "/edgetpu_model.tflite"
        assert frigate_config.detectors["openvino"].model.path == "/etc/hosts"
@@ -956,7 +956,7 @@ class TestConfig(unittest.TestCase):
    def test_merge_labelmap(self):
        config = {
            "mqtt": {"host": "mqtt"},
-            "model": {"labelmap": {7: "truck"}},
+            "models": {"default": {"labelmap": {7: "truck"}}},
            "cameras": {
                "back": {
                    "ffmpeg": {
@@ -977,7 +977,7 @@ class TestConfig(unittest.TestCase):
        }
        frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[7] == "truck"
+        assert frigate_config.models["default"].merged_labelmap[7] == "truck"
    def test_default_labelmap_empty(self):
        config = {
@@ -1002,12 +1002,12 @@ class TestConfig(unittest.TestCase):
        }
        frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[0] == "person"
+        assert frigate_config.models["default"].merged_labelmap[0] == "person"
    def test_default_labelmap(self):
        config = {
            "mqtt": {"host": "mqtt"},
-            "model": {"width": 320, "height": 320},
+            "models": {"default": {"width": 320, "height": 320}},
            "cameras": {
                "back": {
                    "ffmpeg": {
@@ -1028,7 +1028,7 @@ class TestConfig(unittest.TestCase):
        }
        frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[0] == "person"
+        assert frigate_config.models["default"].merged_labelmap[0] == "person"
    def test_plus_labelmap(self):
        with open(os.path.join(MODEL_CACHE_DIR, "test"), "w") as f:
@@ -1039,7 +1039,7 @@ class TestConfig(unittest.TestCase):
        config = {
            "mqtt": {"host": "mqtt"},
            "detectors": {"cpu": {"type": "cpu"}},
-            "model": {"path": "plus://test"},
+            "models": {"default": {"path": "plus://test"}},
            "cameras": {
                "back": {
                    "ffmpeg": {
@@ -1060,7 +1060,7 @@ class TestConfig(unittest.TestCase):
        }
        frigate_config = FrigateConfig(**config)
-        assert frigate_config.model.merged_labelmap[0] == "amazon"
+        assert frigate_config.models["default"].merged_labelmap[0] == "amazon"
    def test_fails_on_invalid_role(self):
        config = {
@@ -1765,5 +1765,177 @@ class TestAttributeFilterDefaults(unittest.TestCase):
        self.assertEqual(face_filter.min_score, 0.3)
 class TestMultiModelConfig(unittest.TestCase):
    """Tests for named models and detector instance assignment."""
    def setUp(self):
        self.base = {
            "mqtt": {"host": "mqtt"},
            "models": {
                "indoor": {"width": 320, "height": 320},
                "outdoor": {"width": 640, "height": 640},
            },
            "cameras": {
                "living_room": self._camera("indoor"),
                "driveway": self._camera("outdoor"),
            },
        }
    def _camera(self, model=None):
        camera = {
            "ffmpeg": {
                "inputs": [{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}]
            },
            "detect": {"height": 1080, "width": 1920, "fps": 5},
        }
        if model:
            camera["detect"]["model"] = model
        return camera
    def test_single_model_resolves_implicitly(self):
        config = FrigateConfig(
            mqtt={"host": "mqtt"},
            models={"custom": {"width": 320, "height": 320}},
            cameras={"back": self._camera()},
        )
        assert config.cameras["back"].detect.model == "custom"
    def test_multiple_models_resolve_to_default(self):
        config = FrigateConfig(
            mqtt={"host": "mqtt"},
            models={
                "default": {"width": 320, "height": 320},
                "outdoor": {"width": 640, "height": 640},
            },
            cameras={"back": self._camera()},
        )
        assert config.cameras["back"].detect.model == "default"
    def test_multiple_models_without_default_requires_selection(self):
        config = self.base.copy()
        config["cameras"] = {"back": self._camera()}
        self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
    def test_camera_references_missing_model(self):
        config = self.base.copy()
        config["cameras"] = {"back": self._camera("thermal")}
        self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
    def test_global_detect_model_inherited_and_overridden(self):
        config = self.base.copy()
        config["detect"] = {"model": "indoor"}
        config["cameras"] = {
            "living_room": self._camera(),
            "driveway": self._camera("outdoor"),
        }
        frigate_config = FrigateConfig(**config)
        assert frigate_config.cameras["living_room"].detect.model == "indoor"
        assert frigate_config.cameras["driveway"].detect.model == "outdoor"
    def test_invalid_model_name(self):
        config = self.base.copy()
        config["models"] = {"bad name!": {"width": 320, "height": 320}}
        self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
    def test_multi_model_detector_expands_instances(self):
        config = self.base.copy()
        config["detectors"] = {
            "ov0": {"type": "openvino", "device": "GPU"},
            "ov1": {"type": "openvino", "device": "GPU.1"},
        }
        frigate_config = FrigateConfig(**config)
        assert sorted(frigate_config.detector_instances.keys()) == [
            "ov0_indoor",
            "ov0_outdoor",
            "ov1_indoor",
            "ov1_outdoor",
        ]
        assert frigate_config.detector_instances["ov0_indoor"].model_key == "indoor"
        assert frigate_config.detector_instances["ov0_indoor"].model.width == 320
        assert frigate_config.detector_instances["ov0_outdoor"].model.width == 640
    def test_multi_model_detector_single_model_keeps_name(self):
        config = self.base.copy()
        config["models"] = {"default": {"width": 320, "height": 320}}
        config["cameras"] = {"back": self._camera()}
        config["detectors"] = {"ov": {"type": "openvino", "device": "GPU"}}
        frigate_config = FrigateConfig(**config)
        assert list(frigate_config.detector_instances.keys()) == ["ov"]
        assert frigate_config.detector_instances["ov"].model_key == "default"
    def test_single_model_detectors_round_robin(self):
        config = self.base.copy()
        config["detectors"] = {
            "coral0": {"type": "edgetpu", "device": "usb:0"},
            "coral1": {"type": "edgetpu", "device": "usb:1"},
            "coral2": {"type": "edgetpu", "device": "usb:2"},
        }
        frigate_config = FrigateConfig(**config)
        assignments = {
            key: instance.model_key
            for key, instance in frigate_config.detector_instances.items()
        }
        assert assignments == {
            "coral0": "indoor",
            "coral1": "outdoor",
            "coral2": "indoor",
        }
    def test_single_model_detectors_insufficient_coverage(self):
        config = self.base.copy()
        config["detectors"] = {"coral": {"type": "edgetpu", "device": "usb"}}
        self.assertRaises(ValidationError, lambda: FrigateConfig(**config))
    def test_single_model_detector_with_multi_model_detector(self):
        config = self.base.copy()
        config["detectors"] = {
            "coral": {"type": "edgetpu", "device": "usb"},
            "ov": {"type": "openvino", "device": "GPU"},
        }
        frigate_config = FrigateConfig(**config)
        assert frigate_config.detector_instances["coral"].model_key == "indoor"
        assert frigate_config.detector_instances["ov_indoor"].model_key == "indoor"
        assert frigate_config.detector_instances["ov_outdoor"].model_key == "outdoor"
    def test_unused_model_gets_no_instances(self):
        config = self.base.copy()
        config["models"] = {
            **config["models"],
            "thermal": {"width": 320, "height": 320},
        }
        frigate_config = FrigateConfig(**config)
        model_keys = {
            instance.model_key
            for instance in frigate_config.detector_instances.values()
        }
        assert "thermal" not in model_keys
    def test_model_path_ignored_when_detector_runs_multiple_models(self):
        config = self.base.copy()
        config["detectors"] = {
            "cpu": {"type": "cpu", "model_path": "/custom_model.tflite"}
        }
        frigate_config = FrigateConfig(**config)
        assert (
            frigate_config.detector_instances["cpu_indoor"].model.path
            == "/cpu_model.tflite"
        )
    def test_model_path_applied_when_detector_runs_one_model(self):
        config = self.base.copy()
        config["models"] = {"default": {"width": 320, "height": 320}}
        config["cameras"] = {"back": self._camera()}
        config["detectors"] = {
            "cpu": {"type": "cpu", "model_path": "/custom_model.tflite"}
        }
        frigate_config = FrigateConfig(**config)
        assert (
            frigate_config.detector_instances["cpu"].model.path
            == "/custom_model.tflite"
        )
 if __name__ == "__main__":
    unittest.main(verbosity=2)
@@ -0,0 +1,42 @@
 """Tests for config file migration functions."""
 import unittest
 from frigate.util.config import migrate_019_0
 class TestMigrate019(unittest.TestCase):
    def test_migrates_model_to_named_models(self):
        config = {
            "version": "0.18-0",
            "mqtt": {"host": "mqtt"},
            "model": {"path": "/config/model.tflite", "width": 320, "height": 320},
        }
        new_config = migrate_019_0(config)
        assert "model" not in new_config
        assert new_config["models"] == {
            "default": {"path": "/config/model.tflite", "width": 320, "height": 320}
        }
        assert new_config["version"] == "0.19-0"
    def test_no_model_defined(self):
        config = {"version": "0.18-0", "mqtt": {"host": "mqtt"}}
        new_config = migrate_019_0(config)
        assert "model" not in new_config
        assert "models" not in new_config
        assert new_config["version"] == "0.19-0"
    def test_existing_models_not_overwritten(self):
        config = {
            "version": "0.18-0",
            "model": {"width": 320},
            "models": {"custom": {"width": 640}},
        }
        new_config = migrate_019_0(config)
        assert "model" not in new_config
        assert new_config["models"] == {"custom": {"width": 640}}
        assert new_config["version"] == "0.19-0"
 if __name__ == "__main__":
    unittest.main(verbosity=2)
@@ -209,7 +209,10 @@ class TrackedObjectProcessor(threading.Thread):
                    if obj.obj_data.get("sub_label"):
                        sub_label = obj.obj_data["sub_label"][0]
-                        if sub_label in self.config.model.all_attribute_logos:
+                        if (
                            sub_label
                            in self.config.model_for_camera(camera).all_attribute_logos
                        ):
                            self.dispatcher.publish(
                                f"{camera}/{sub_label}/snapshot",
                                jpg_bytes,
@@ -20,7 +20,7 @@ from frigate.util.services import get_video_properties
 logger = logging.getLogger(__name__)
-CURRENT_CONFIG_VERSION = "0.18-0"
+CURRENT_CONFIG_VERSION = "0.19-0"
 DEFAULT_CONFIG_FILE = os.path.join(CONFIG_DIR, "config.yml")
@@ -99,6 +99,7 @@ def migrate_frigate_config(config_file: str):
        new_config = migrate_014(config)
        with open(config_file, "w") as f:
            yaml.dump(new_config, f)
        config = new_config
        previous_version = "0.14"
        logger.info("Migrating export file names...")
@@ -117,6 +118,7 @@ def migrate_frigate_config(config_file: str):
        new_config = migrate_015_0(config)
        with open(config_file, "w") as f:
            yaml.dump(new_config, f)
        config = new_config
        previous_version = "0.15-0"
    if previous_version < "0.15-1":
@@ -124,6 +126,7 @@ def migrate_frigate_config(config_file: str):
        new_config = migrate_015_1(config)
        with open(config_file, "w") as f:
            yaml.dump(new_config, f)
        config = new_config
        previous_version = "0.15-1"
    if previous_version < "0.16-0":
@@ -131,6 +134,7 @@ def migrate_frigate_config(config_file: str):
        new_config = migrate_016_0(config)
        with open(config_file, "w") as f:
            yaml.dump(new_config, f)
        config = new_config
        previous_version = "0.16-0"
    if previous_version < "0.17-0":
@@ -138,6 +142,7 @@ def migrate_frigate_config(config_file: str):
        new_config = migrate_017_0(config)
        with open(config_file, "w") as f:
            yaml.dump(new_config, f)
        config = new_config
        previous_version = "0.17-0"
    if previous_version < "0.18-0":
@@ -145,8 +150,17 @@ def migrate_frigate_config(config_file: str):
        new_config = migrate_018_0(config)
        with open(config_file, "w") as f:
            yaml.dump(new_config, f)
        config = new_config
        previous_version = "0.18-0"
    if previous_version < "0.19-0":
        logger.info(f"Migrating frigate config from {previous_version} to 0.19-0...")
        new_config = migrate_019_0(config)
        with open(config_file, "w") as f:
            yaml.dump(new_config, f)
        config = new_config
        previous_version = "0.19-0"
    logger.info("Finished frigate config migration...")
@@ -658,6 +672,20 @@ def migrate_018_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]
    return new_config
 def migrate_019_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]]:
    """Handle migrating frigate config to 0.19-0"""
    new_config = config.copy()
    # Migrate the single model config to named models
    model = new_config.pop("model", None)
    if model is not None and "models" not in new_config:
        new_config["models"] = {"default": model}
    new_config["version"] = "0.19-0"
    return new_config
 def get_relative_coordinates(
    mask: str | list | None,
    frame_shape: tuple[int, int],
@@ -1 +1 @@
-[{"id": "case-001", "name": "Package Theft Investigation", "description": "Review of suspicious activity near the front porch", "created_at": 1780597809.365581, "updated_at": 1780673409.365581}]
+[{"id": "case-001", "name": "Package Theft Investigation", "description": "Review of suspicious activity near the front porch", "created_at": 1784761296.1184616, "updated_at": 1784836896.1184616}]
@@ -1 +1 @@
-[{"id": "event-person-001", "label": "person", "sub_label": null, "camera": "front_door", "start_time": 1780677009.365581, "end_time": 1780677039.365581, "false_positive": false, "zones": ["front_yard"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "abc123", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.92, "score": 0.92, "region": [0.1, 0.1, 0.5, 0.8], "box": [0.2, 0.15, 0.45, 0.75], "area": 0.18, "ratio": 0.6, "type": "object", "description": "A person walking toward the front door", "average_estimated_speed": 1.2, "velocity_angle": 45.0, "path_data": [[[0.2, 0.5], 0.0], [[0.3, 0.5], 1.0]]}}, {"id": "event-car-001", "label": "car", "sub_label": null, "camera": "backyard", "start_time": 1780673409.365581, "end_time": 1780673454.365581, "false_positive": false, "zones": ["driveway"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "def456", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.87, "score": 0.87, "region": [0.3, 0.2, 0.9, 0.7], "box": [0.35, 0.25, 0.85, 0.65], "area": 0.2, "ratio": 1.25, "type": "object", "description": "A car parked in the driveway", "average_estimated_speed": 0.0, "velocity_angle": 0.0, "path_data": []}}, {"id": "event-person-002", "label": "person", "sub_label": null, "camera": "garage", "start_time": 1780669809.365581, "end_time": 1780669829.365581, "false_positive": false, "zones": [], "thumbnail": null, "has_clip": false, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "ghi789", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.78, "score": 0.78, "region": [0.0, 0.0, 0.6, 0.9], "box": [0.1, 0.05, 0.5, 0.85], "area": 0.32, "ratio": 0.5, "type": "object", "description": null, "average_estimated_speed": 0.5, "velocity_angle": 90.0, "path_data": [[[0.1, 0.4], 0.0]]}}]
+[{"id": "event-person-001", "label": "person", "sub_label": null, "camera": "front_door", "start_time": 1784840496.1184616, "end_time": 1784840526.1184616, "false_positive": false, "zones": ["front_yard"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "abc123", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.92, "score": 0.92, "region": [0.1, 0.1, 0.5, 0.8], "box": [0.2, 0.15, 0.45, 0.75], "area": 0.18, "ratio": 0.6, "type": "object", "description": "A person walking toward the front door", "average_estimated_speed": 1.2, "velocity_angle": 45.0, "path_data": [[[0.2, 0.5], 0.0], [[0.3, 0.5], 1.0]]}}, {"id": "event-car-001", "label": "car", "sub_label": null, "camera": "backyard", "start_time": 1784836896.1184616, "end_time": 1784836941.1184616, "false_positive": false, "zones": ["driveway"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "def456", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.87, "score": 0.87, "region": [0.3, 0.2, 0.9, 0.7], "box": [0.35, 0.25, 0.85, 0.65], "area": 0.2, "ratio": 1.25, "type": "object", "description": "A car parked in the driveway", "average_estimated_speed": 0.0, "velocity_angle": 0.0, "path_data": []}}, {"id": "event-person-002", "label": "person", "sub_label": null, "camera": "garage", "start_time": 1784833296.1184616, "end_time": 1784833316.1184616, "false_positive": false, "zones": [], "thumbnail": null, "has_clip": false, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "ghi789", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.78, "score": 0.78, "region": [0.0, 0.0, 0.6, 0.9], "box": [0.1, 0.05, 0.5, 0.85], "area": 0.32, "ratio": 0.5, "type": "object", "description": null, "average_estimated_speed": 0.5, "velocity_angle": 90.0, "path_data": [[[0.1, 0.4], 0.0]]}}]
@@ -1 +1 @@
-[{"id": "export-001", "camera": "front_door", "name": "Front Door - Person Alert", "date": 1780680609.365581, "video_path": "/exports/export-001.mp4", "thumb_path": "/exports/export-001-thumb.jpg", "in_progress": false, "export_case_id": null}, {"id": "export-002", "camera": "backyard", "name": "Backyard - Car Detection", "date": 1780673409.365581, "video_path": "/exports/export-002.mp4", "thumb_path": "/exports/export-002-thumb.jpg", "in_progress": false, "export_case_id": "case-001"}, {"id": "export-003", "camera": "garage", "name": "Garage - In Progress", "date": 1780682409.365581, "video_path": "/exports/export-003.mp4", "thumb_path": "/exports/export-003-thumb.jpg", "in_progress": true, "export_case_id": null}]
+[{"id": "export-001", "camera": "front_door", "name": "Front Door - Person Alert", "date": 1784844096.1184616, "video_path": "/exports/export-001.mp4", "thumb_path": "/exports/export-001-thumb.jpg", "in_progress": false, "export_case_id": null}, {"id": "export-002", "camera": "backyard", "name": "Backyard - Car Detection", "date": 1784836896.1184616, "video_path": "/exports/export-002.mp4", "thumb_path": "/exports/export-002-thumb.jpg", "in_progress": false, "export_case_id": "case-001"}, {"id": "export-003", "camera": "garage", "name": "Garage - In Progress", "date": 1784845896.1184616, "video_path": "/exports/export-003.mp4", "thumb_path": "/exports/export-003-thumb.jpg", "in_progress": true, "export_case_id": null}]
@@ -102,11 +102,17 @@ def generate_config():
        snapshot = config.model_dump()
    # Runtime-computed fields not in the Pydantic dump
-    all_attrs = set()
+    for model_dict in snapshot.get("models", {}).values():
-    for attrs in snapshot.get("model", {}).get("attributes_map", {}).values():
+        all_attrs = set()
-        all_attrs.update(attrs)
+        for attrs in model_dict.get("attributes_map", {}).values():
-    snapshot["model"]["all_attributes"] = sorted(all_attrs)
+            all_attrs.update(attrs)
-    snapshot["model"]["colormap"] = {}
+        model_dict["all_attributes"] = sorted(all_attrs)
        model_dict["colormap"] = {}
    # legacy single-model block mirrors the default model, matching /api/config
    models = snapshot.get("models", {})
    default_key = "default" if "default" in models else next(iter(models))
    snapshot["model"] = models[default_key]
    return snapshot
@@ -1 +1 @@
-{"2026-06-05": {"day": "2026-06-05", "reviewed_alert": 1, "reviewed_detection": 0, "total_alert": 2, "total_detection": 2}, "2026-06-04": {"day": "2026-06-04", "reviewed_alert": 3, "reviewed_detection": 2, "total_alert": 3, "total_detection": 4}}
+{"2026-07-23": {"day": "2026-07-23", "reviewed_alert": 1, "reviewed_detection": 0, "total_alert": 2, "total_detection": 2}, "2026-07-22": {"day": "2026-07-22", "reviewed_alert": 3, "reviewed_detection": 2, "total_alert": 3, "total_detection": 4}}
@@ -1 +1 @@
-[{"id": "review-alert-001", "camera": "front_door", "start_time": "2026-06-05T11:30:09.365581", "end_time": "2026-06-05T11:30:39.365581", "has_been_reviewed": false, "severity": "alert", "thumb_path": "/clips/front_door/review-alert-001-thumb.jpg", "data": {"audio": [], "detections": ["person-abc123"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}, {"id": "review-alert-002", "camera": "backyard", "start_time": "2026-06-05T10:30:09.365581", "end_time": "2026-06-05T10:30:54.365581", "has_been_reviewed": true, "severity": "alert", "thumb_path": "/clips/backyard/review-alert-002-thumb.jpg", "data": {"audio": [], "detections": ["car-def456"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["driveway"]}}, {"id": "review-detect-001", "camera": "garage", "start_time": "2026-06-05T09:30:09.365581", "end_time": "2026-06-05T09:30:29.365581", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/garage/review-detect-001-thumb.jpg", "data": {"audio": [], "detections": ["person-ghi789"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": []}}, {"id": "review-detect-002", "camera": "front_door", "start_time": "2026-06-05T08:30:09.365581", "end_time": "2026-06-05T08:30:24.365581", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/front_door/review-detect-002-thumb.jpg", "data": {"audio": [], "detections": ["car-jkl012"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}]
+[{"id": "review-alert-001", "camera": "front_door", "start_time": "2026-07-23T21:01:36.118462", "end_time": "2026-07-23T21:02:06.118462", "has_been_reviewed": false, "severity": "alert", "thumb_path": "/clips/front_door/review-alert-001-thumb.jpg", "data": {"audio": [], "detections": ["person-abc123"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}, {"id": "review-alert-002", "camera": "backyard", "start_time": "2026-07-23T20:01:36.118462", "end_time": "2026-07-23T20:02:21.118462", "has_been_reviewed": true, "severity": "alert", "thumb_path": "/clips/backyard/review-alert-002-thumb.jpg", "data": {"audio": [], "detections": ["car-def456"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["driveway"]}}, {"id": "review-detect-001", "camera": "garage", "start_time": "2026-07-23T19:01:36.118462", "end_time": "2026-07-23T19:01:56.118462", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/garage/review-detect-001-thumb.jpg", "data": {"audio": [], "detections": ["person-ghi789"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": []}}, {"id": "review-detect-002", "camera": "front_door", "start_time": "2026-07-23T18:01:36.118462", "end_time": "2026-07-23T18:01:51.118462", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/front_door/review-detect-002-thumb.jpg", "data": {"audio": [], "detections": ["car-jkl012"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}]
@@ -86,6 +86,10 @@
      "label": "Enable object detection",
      "description": "Enable or disable object detection for this camera."
    },
    "model": {
      "label": "Detection model name",
      "description": "Name of the model (key under `models`) used by this camera. Defaults to the only defined model, or the model named 'default'."
    },
    "height": {
      "label": "Detect height",
      "description": "Height (pixels) of frames used for the detect stream; leave empty to use the native stream resolution."
@@ -329,6 +329,10 @@
      "label": "Detector specific model path",
      "description": "File path to the detector model binary if required by the chosen detector."
    },
    "model_key": {
      "label": "Assigned model name",
      "description": "Name of the model (key under `models`) this detector instance serves. Set automatically at runtime, users should not set this."
    },
    "axengine": {
      "label": "AXEngine NPU",
      "description": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime."
@@ -454,9 +458,9 @@
      }
    }
  },
-  "model": {
+  "models": {
-    "label": "Detection model",
+    "label": "Detection models",
-    "description": "Settings to configure a custom object detection model and its input shape.",
+    "description": "Named object detection models. Cameras select a model with detect.model; detectors are assigned to models automatically.",
    "path": {
      "label": "Custom object detector model path",
      "description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
@@ -625,6 +629,10 @@
      "label": "Enable object detection",
      "description": "Enable or disable object detection for all cameras; can be overridden per-camera."
    },
    "model": {
      "label": "Detection model name",
      "description": "Name of the model (key under `models`) used by this camera. Defaults to the only defined model, or the model named 'default'."
    },
    "height": {
      "label": "Detect height",
      "description": "Height (pixels) of frames used for the detect stream; leave empty to use the native stream resolution."
@@ -74,6 +74,7 @@ const SECTIONS_WITHOUT_OVERRIDE_BADGE = new Set([
  "birdseye",
  "detectors",
  "model",
  "models",
 ]);
 type CameraEntryProps = {
@@ -70,7 +70,17 @@ export function extractSectionSchema(
    // For global level, get from root properties
    if (schemaObj.properties) {
      const props = schemaObj.properties;
-      const sectionProp = props[sectionPath];
+      let sectionProp = props[sectionPath];
      // the model editor edits a single entry of the `models` map, so
      // resolve the map value schema since there is no root `model` property
      if (!sectionProp && sectionPath === "model") {
        const modelsProp = props["models"] as SchemaWithDefinitions | undefined;
        const additional = modelsProp?.additionalProperties;
        if (additional && typeof additional === "object") {
          sectionProp = additional as RJSFSchema;
        }
      }
      if (sectionProp && typeof sectionProp === "object") {
        if ("$ref" in sectionProp && typeof sectionProp.$ref === "string") {
@@ -59,6 +59,7 @@ export interface CameraConfig {
    height: number;
    max_disappeared: number;
    min_initialized: number;
    model: string;
    stationary: {
      interval: number;
      max_frames: {
@@ -394,6 +395,30 @@ export type GenAIAgentConfig = {
  runtime_options?: Record<string, unknown>;
 };
 export interface ModelConfig {
  height: number;
  input_pixel_format: string;
  input_tensor: string;
  labelmap: Record<string, unknown>;
  labelmap_path: string | null;
  model_type: string;
  path: string | null;
  width: number;
  colormap: { [key: string]: [number, number, number] };
  attributes_map: { [key: string]: string[] };
  all_attributes: string[];
  plus?: {
    name: string;
    id: string;
    trainDate: string;
    baseModel: string;
    isBaseModel: boolean;
    supportedDetectors: string[];
    width: number;
    height: number;
  } | null;
 }
 export interface FrigateConfig {
  version: string;
  safe_mode: boolean;
@@ -446,6 +471,7 @@ export interface FrigateConfig {
    height: number | null;
    max_disappeared: number | null;
    min_initialized: number | null;
    model: string | null;
    stationary: {
      interval: number | null;
      max_frames: {
@@ -512,29 +538,10 @@ export interface FrigateConfig {
    logs: Record<string, string>;
  };
-  model: {
+  // legacy single-model block, mirrors the default entry of `models`
-    height: number;
+  model: ModelConfig;
-    input_pixel_format: string;
+
-    input_tensor: string;
+  models: { [modelKey: string]: ModelConfig };
    labelmap: Record<string, unknown>;
    labelmap_path: string | null;
    model_type: string;
    path: string | null;
    width: number;
    colormap: { [key: string]: [number, number, number] };
    attributes_map: { [key: string]: string[] };
    all_attributes: string[];
    plus?: {
      name: string;
      id: string;
      trainDate: string;
      baseModel: string;
      isBaseModel: boolean;
      supportedDetectors: string[];
      width: number;
      height: number;
    } | null;
  };
  motion: Record<string, unknown> | null;
@@ -115,8 +115,10 @@ const STATUS_BAR_KEY = "detectors_and_model";
 const EMPTY_PENDING: Record<string, ConfigSectionData> = {};
 const deriveInitialState = (config: FrigateConfig): PageState => {
-  const plusModelId = config.model?.plus?.id;
+  // this view edits the default model; other named models are untouched
-  const modelPath = config.model?.path;
+  const defaultModel = config.models?.default ?? config.model;
  const plusModelId = defaultModel?.plus?.id;
  const modelPath = defaultModel?.path;
  const plusEnabled = Boolean(config.plus?.enabled);
  // The reliable signal that a Plus model is currently active is the
@@ -136,10 +138,8 @@ const deriveInitialState = (config: FrigateConfig): PageState => {
    modelTab = "custom";
  }
-  const { plus: _plus, ...modelWithoutPlus } = (config.model ?? {}) as Record<
+  const { plus: _plus, ...modelWithoutPlus } = (defaultModel ??
-    string,
+    {}) as unknown as Record<string, unknown>;
    unknown
  >;
  // If a Plus model is active, the resolved `model.path` is auto-derived from
  // `plus.id` — drop it so the Custom tab starts clean and doesn't silently
  // re-save the same Plus model when the user thinks they switched modes.
@@ -476,7 +476,7 @@ export default function DetectorsAndModelSettingsView({
        try {
          await axios.put("config/set", {
            requires_restart: 0,
-            config_data: { detectors: null, model: null },
+            config_data: { detectors: null, models: { default: null } },
          });
          preCleared = true;
        } catch {
@@ -488,7 +488,7 @@ export default function DetectorsAndModelSettingsView({
        requires_restart: 0,
        config_data: {
          detectors: sanitizedDetectors,
-          model: modelPayload,
+          models: { default: modelPayload },
        },
      });
@@ -541,7 +541,7 @@ export default function DetectorsAndModelSettingsView({
                snapshot.detectors,
                detectorHiddenFields,
              ),
-              model: restoreModel,
+              models: { default: restoreModel },
            },
          });
        } catch {
		`@@ -1 +1 @@`
			`[{"id": "case-001", "name": "Package Theft Investigation", "description": "Review of suspicious activity near the front porch", "created_at": 1780597809.365581, "updated_at": 1780673409.365581}]`				`[{"id": "case-001", "name": "Package Theft Investigation", "description": "Review of suspicious activity near the front porch", "created_at": 1784761296.1184616, "updated_at": 1784836896.1184616}]`
		`@@ -1 +1 @@`
			[{"id": "event-person-001", "label": "person", "sub_label": null, "camera": "front_door", "start_time": 1780677009.365581, "end_time": 1780677039.365581, "false_positive": false, "zones": ["front_yard"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "abc123", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.92, "score": 0.92, "region": [0.1, 0.1, 0.5, 0.8], "box": [0.2, 0.15, 0.45, 0.75], "area": 0.18, "ratio": 0.6, "type": "object", "description": "A person walking toward the front door", "average_estimated_speed": 1.2, "velocity_angle": 45.0, "path_data": [[[0.2, 0.5], 0.0], [[0.3, 0.5], 1.0]]}}, {"id": "event-car-001", "label": "car", "sub_label": null, "camera": "backyard", "start_time": 1780673409.365581, "end_time": 1780673454.365581, "false_positive": false, "zones": ["driveway"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "def456", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.87, "score": 0.87, "region": [0.3, 0.2, 0.9, 0.7], "box": [0.35, 0.25, 0.85, 0.65], "area": 0.2, "ratio": 1.25, "type": "object", "description": "A car parked in the driveway", "average_estimated_speed": 0.0, "velocity_angle": 0.0, "path_data": []}}, {"id": "event-person-002", "label": "person", "sub_label": null, "camera": "garage", "start_time": 1780669809.365581, "end_time": 1780669829.365581, "false_positive": false, "zones": [], "thumbnail": null, "has_clip": false, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "ghi789", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.78, "score": 0.78, "region": [0.0, 0.0, 0.6, 0.9], "box": [0.1, 0.05, 0.5, 0.85], "area": 0.32, "ratio": 0.5, "type": "object", "description": null, "average_estimated_speed": 0.5, "velocity_angle": 90.0, "path_data": [[[0.1, 0.4], 0.0]]}}]				[{"id": "event-person-001", "label": "person", "sub_label": null, "camera": "front_door", "start_time": 1784840496.1184616, "end_time": 1784840526.1184616, "false_positive": false, "zones": ["front_yard"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "abc123", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.92, "score": 0.92, "region": [0.1, 0.1, 0.5, 0.8], "box": [0.2, 0.15, 0.45, 0.75], "area": 0.18, "ratio": 0.6, "type": "object", "description": "A person walking toward the front door", "average_estimated_speed": 1.2, "velocity_angle": 45.0, "path_data": [[[0.2, 0.5], 0.0], [[0.3, 0.5], 1.0]]}}, {"id": "event-car-001", "label": "car", "sub_label": null, "camera": "backyard", "start_time": 1784836896.1184616, "end_time": 1784836941.1184616, "false_positive": false, "zones": ["driveway"], "thumbnail": null, "has_clip": true, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "def456", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.87, "score": 0.87, "region": [0.3, 0.2, 0.9, 0.7], "box": [0.35, 0.25, 0.85, 0.65], "area": 0.2, "ratio": 1.25, "type": "object", "description": "A car parked in the driveway", "average_estimated_speed": 0.0, "velocity_angle": 0.0, "path_data": []}}, {"id": "event-person-002", "label": "person", "sub_label": null, "camera": "garage", "start_time": 1784833296.1184616, "end_time": 1784833316.1184616, "false_positive": false, "zones": [], "thumbnail": null, "has_clip": false, "has_snapshot": true, "retain_indefinitely": false, "plus_id": null, "model_hash": "ghi789", "detector_type": "cpu", "model_type": "ssd", "data": {"top_score": 0.78, "score": 0.78, "region": [0.0, 0.0, 0.6, 0.9], "box": [0.1, 0.05, 0.5, 0.85], "area": 0.32, "ratio": 0.5, "type": "object", "description": null, "average_estimated_speed": 0.5, "velocity_angle": 90.0, "path_data": [[[0.1, 0.4], 0.0]]}}]
		`@@ -1 +1 @@`
			[{"id": "export-001", "camera": "front_door", "name": "Front Door - Person Alert", "date": 1780680609.365581, "video_path": "/exports/export-001.mp4", "thumb_path": "/exports/export-001-thumb.jpg", "in_progress": false, "export_case_id": null}, {"id": "export-002", "camera": "backyard", "name": "Backyard - Car Detection", "date": 1780673409.365581, "video_path": "/exports/export-002.mp4", "thumb_path": "/exports/export-002-thumb.jpg", "in_progress": false, "export_case_id": "case-001"}, {"id": "export-003", "camera": "garage", "name": "Garage - In Progress", "date": 1780682409.365581, "video_path": "/exports/export-003.mp4", "thumb_path": "/exports/export-003-thumb.jpg", "in_progress": true, "export_case_id": null}]				[{"id": "export-001", "camera": "front_door", "name": "Front Door - Person Alert", "date": 1784844096.1184616, "video_path": "/exports/export-001.mp4", "thumb_path": "/exports/export-001-thumb.jpg", "in_progress": false, "export_case_id": null}, {"id": "export-002", "camera": "backyard", "name": "Backyard - Car Detection", "date": 1784836896.1184616, "video_path": "/exports/export-002.mp4", "thumb_path": "/exports/export-002-thumb.jpg", "in_progress": false, "export_case_id": "case-001"}, {"id": "export-003", "camera": "garage", "name": "Garage - In Progress", "date": 1784845896.1184616, "video_path": "/exports/export-003.mp4", "thumb_path": "/exports/export-003-thumb.jpg", "in_progress": true, "export_case_id": null}]
		`@@ -1 +1 @@`
			`{"2026-06-05": {"day": "2026-06-05", "reviewed_alert": 1, "reviewed_detection": 0, "total_alert": 2, "total_detection": 2}, "2026-06-04": {"day": "2026-06-04", "reviewed_alert": 3, "reviewed_detection": 2, "total_alert": 3, "total_detection": 4}}`				`{"2026-07-23": {"day": "2026-07-23", "reviewed_alert": 1, "reviewed_detection": 0, "total_alert": 2, "total_detection": 2}, "2026-07-22": {"day": "2026-07-22", "reviewed_alert": 3, "reviewed_detection": 2, "total_alert": 3, "total_detection": 4}}`
		`@@ -1 +1 @@`
			[{"id": "review-alert-001", "camera": "front_door", "start_time": "2026-06-05T11:30:09.365581", "end_time": "2026-06-05T11:30:39.365581", "has_been_reviewed": false, "severity": "alert", "thumb_path": "/clips/front_door/review-alert-001-thumb.jpg", "data": {"audio": [], "detections": ["person-abc123"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}, {"id": "review-alert-002", "camera": "backyard", "start_time": "2026-06-05T10:30:09.365581", "end_time": "2026-06-05T10:30:54.365581", "has_been_reviewed": true, "severity": "alert", "thumb_path": "/clips/backyard/review-alert-002-thumb.jpg", "data": {"audio": [], "detections": ["car-def456"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["driveway"]}}, {"id": "review-detect-001", "camera": "garage", "start_time": "2026-06-05T09:30:09.365581", "end_time": "2026-06-05T09:30:29.365581", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/garage/review-detect-001-thumb.jpg", "data": {"audio": [], "detections": ["person-ghi789"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": []}}, {"id": "review-detect-002", "camera": "front_door", "start_time": "2026-06-05T08:30:09.365581", "end_time": "2026-06-05T08:30:24.365581", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/front_door/review-detect-002-thumb.jpg", "data": {"audio": [], "detections": ["car-jkl012"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}]				[{"id": "review-alert-001", "camera": "front_door", "start_time": "2026-07-23T21:01:36.118462", "end_time": "2026-07-23T21:02:06.118462", "has_been_reviewed": false, "severity": "alert", "thumb_path": "/clips/front_door/review-alert-001-thumb.jpg", "data": {"audio": [], "detections": ["person-abc123"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}, {"id": "review-alert-002", "camera": "backyard", "start_time": "2026-07-23T20:01:36.118462", "end_time": "2026-07-23T20:02:21.118462", "has_been_reviewed": true, "severity": "alert", "thumb_path": "/clips/backyard/review-alert-002-thumb.jpg", "data": {"audio": [], "detections": ["car-def456"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["driveway"]}}, {"id": "review-detect-001", "camera": "garage", "start_time": "2026-07-23T19:01:36.118462", "end_time": "2026-07-23T19:01:56.118462", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/garage/review-detect-001-thumb.jpg", "data": {"audio": [], "detections": ["person-ghi789"], "objects": ["person"], "sub_labels": [], "significant_motion_areas": [], "zones": []}}, {"id": "review-detect-002", "camera": "front_door", "start_time": "2026-07-23T18:01:36.118462", "end_time": "2026-07-23T18:01:51.118462", "has_been_reviewed": false, "severity": "detection", "thumb_path": "/clips/front_door/review-detect-002-thumb.jpg", "data": {"audio": [], "detections": ["car-jkl012"], "objects": ["car"], "sub_labels": [], "significant_motion_areas": [], "zones": ["front_yard"]}}]