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Implement multi-model object detection

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This commit is contained in:
Nicolas Mowen
2026-07-23 17:51:21 -06:00
parent e3fa701893
commit ede06d794d
49 changed files with 1070 additions and 473 deletions
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docs/data/object_detectors_models.yaml
+236 -212
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@@ -34,12 +34,13 @@ edgeTPU:
type: edgetpu
device: usb
model:
model_type: yolo-generic
width: 320 # <--- should match the imgsize of the model, typically 320
height: 320 # <--- should match the imgsize of the model, typically 320
path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
labelmap_path: /config/labels-coco17.txt
models:
default:
model_type: yolo-generic
width: 320 # <--- should match the imgsize of the model, typically 320
height: 320 # <--- should match the imgsize of the model, typically 320
path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
labelmap_path: /config/labels-coco17.txt
hailo8l:
title: Hailo-8/Hailo-8L
models:
@@ -67,27 +68,28 @@ hailo8l:
type: hailo8l
device: PCIe
model:
width: 320
height: 320
input_tensor: nhwc
input_pixel_format: rgb
input_dtype: int
model_type: yolo-generic
labelmap_path: /labelmap/coco-80.txt
models:
default:
width: 320
height: 320
input_tensor: nhwc
input_pixel_format: rgb
input_dtype: int
model_type: yolo-generic
labelmap_path: /labelmap/coco-80.txt
# The detector automatically selects the default model based on your hardware:
# - For Hailo-8 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.
# If a local path is provided and the file exists, it will be used instead of downloading.
# Example:
# path: /config/model_cache/hailo/yolov6n.hef
#
# 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
# just make sure to give it the write configuration based on the model
# The detector automatically selects the default model based on your hardware:
# - For Hailo-8 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.
# If a local path is provided and the file exists, it will be used instead of downloading.
# Example:
# path: /config/model_cache/hailo/yolov6n.hef
#
# 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
# 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:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: rgb
model_type: ssd
# Specify the local model path (if available) or URL for SSD MobileNet v1.
# 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
models:
default:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: rgb
model_type: ssd
# Specify the local model path (if available) or URL for SSD MobileNet v1.
# 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
openvino:
title: OpenVINO
models:
@@ -171,14 +174,15 @@ openvino:
type: openvino
device: GPU # or NPU
model:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolo.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
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:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
models:
default:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
- key: yolo-legacy
label: YOLO (v3, v4, v7)
recommended: false
@@ -240,14 +245,15 @@ openvino:
type: openvino
device: GPU # or NPU
model:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolo.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
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:
model_type: yolonas
width: 320 # <--- should match whatever was set in notebook
height: 320 # <--- should match whatever was set in notebook
input_tensor: nchw
input_pixel_format: bgr
path: /config/yolo_nas_s.onnx
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolonas
width: 320 # <--- should match whatever was set in notebook
height: 320 # <--- should match whatever was set in notebook
input_tensor: nchw
input_pixel_format: bgr
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:
model_type: yolox
path: /config/model_cache/yolox.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolox
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:
model_type: rfdetr
width: 320
height: 320
input_tensor: nchw
input_dtype: float
path: /config/model_cache/rfdetr.onnx # use the filename you generated above
models:
default:
model_type: rfdetr
width: 320
height: 320
input_tensor: nchw
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:
model_type: dfine
width: 640
height: 640
input_tensor: nchw
input_dtype: float
path: /config/model_cache/dfine-s.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: dfine
width: 640
height: 640
input_tensor: nchw
input_dtype: float
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:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolo.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
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:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolo.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
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:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolo.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
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:
model_type: rfdetr
width: 320
height: 320
input_tensor: nchw
input_dtype: float
path: /config/model_cache/rfdetr.onnx # use the filename you generated above
models:
default:
model_type: rfdetr
width: 320
height: 320
input_tensor: nchw
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:
model_type: yolonas
width: 320 # <--- should match whatever was set in notebook
height: 320 # <--- should match whatever was set in notebook
input_pixel_format: bgr
input_tensor: nchw
path: /config/yolo_nas_s.onnx
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolonas
width: 320 # <--- should match whatever was set in notebook
height: 320 # <--- should match whatever was set in notebook
input_pixel_format: bgr
input_tensor: nchw
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:
model_type: yolox
width: 416 # <--- should match the imgsize set during model export
height: 416 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float_denorm
path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolox
width: 416 # <--- should match the imgsize set during model export
height: 416 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float_denorm
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:
model_type: dfine
width: 640
height: 640
input_tensor: nchw
input_dtype: float
path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: dfine
width: 640
height: 640
input_tensor: nchw
input_dtype: float
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:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolo.onnx # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
models:
default:
model_type: yolo-generic
width: 320 # <--- should match the imgsize set during model export
height: 320 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
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:
model_type: yolonas
width: 320 # (Can be set to 640 for higher resolution)
height: 320 # (Can be set to 640 for higher resolution)
input_tensor: nchw
input_dtype: float
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolonas.zip
# The .zip file must contain:
# ├── yolonas.dfp (a file ending with .dfp)
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
models:
default:
model_type: yolonas
width: 320 # (Can be set to 640 for higher resolution)
height: 320 # (Can be set to 640 for higher resolution)
input_tensor: nchw
input_dtype: float
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolonas.zip
# The .zip file must contain:
# ├── 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:
model_type: yolo-generic
width: 320 # (Can be set to 640 for higher resolution)
height: 320 # (Can be set to 640 for higher resolution)
input_tensor: nchw
input_dtype: float
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolov9.zip
# The .zip file must contain:
# ├── yolov9.dfp (a file ending with .dfp)
models:
default:
model_type: yolo-generic
width: 320 # (Can be set to 640 for higher resolution)
height: 320 # (Can be set to 640 for higher resolution)
input_tensor: nchw
input_dtype: float
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolov9.zip
# 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:
model_type: yolox
width: 640
height: 640
input_tensor: nchw
input_dtype: float_denorm
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolox.zip
# The .zip file must contain:
# ├── yolox.dfp (a file ending with .dfp)
models:
default:
model_type: yolox
width: 640
height: 640
input_tensor: nchw
input_dtype: float_denorm
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolox.zip
# 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:
model_type: ssd
width: 320
height: 320
input_tensor: nchw
input_dtype: float
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/ssdlite_mobilenet.zip
# The .zip file must contain:
# ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
# └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
models:
default:
model_type: ssd
width: 320
height: 320
input_tensor: nchw
input_dtype: float
labelmap_path: /labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/ssdlite_mobilenet.zip
# The .zip file must contain:
# ├── 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:
path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
input_tensor: nchw
input_pixel_format: rgb
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
models:
default:
path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
labelmap_path: /labelmap/coco-80.txt
input_tensor: nchw
input_pixel_format: rgb
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:
path: frigate-yolov9-tiny
model_type: yolo-generic
width: 320
height: 320
input_dtype: int
input_pixel_format: bgr
labelmap_path: /labelmap/coco-80.txt
models:
default:
path: frigate-yolov9-tiny
model_type: yolo-generic
width: 320
height: 320
input_dtype: int
input_pixel_format: bgr
labelmap_path: /labelmap/coco-80.txt
degirumAiServer:
title: DeGirum AI Server
models:
docs/docs/configuration/advanced/reference.md
+46 -36
View File
@@ -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:
# Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
path: /edgetpu_model.tflite
# Required: path to the labelmap (default: shown below)
labelmap_path: /labelmap.txt
# Required: Object detection model input width (default: shown below)
width: 320
# Required: Object detection model input height (default: shown below)
height: 320
# Required: Object detection model input colorspace
# Valid values are rgb, bgr, or yuv. (default: shown below)
input_pixel_format: rgb
# Required: Object detection model input tensor format
# Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
input_tensor: nhwc
# Optional: Data type of the model input tensor
# Valid values are float, float_denorm, or int (default: shown below)
input_dtype: int
# Required: Object detection model architecture, used by detectors that support more
# than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
# Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
model_type: ssd
# Required: Label name modifications. These are merged into the standard labelmap.
labelmap:
2: vehicle
# Optional: Map of object labels to their attribute labels (default: depends on model)
attributes_map:
person:
- amazon
- face
car:
- amazon
- fedex
- license_plate
- ups
models:
default:
# Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
path: /edgetpu_model.tflite
# Required: path to the labelmap (default: shown below)
labelmap_path: /labelmap.txt
# Required: Object detection model input width (default: shown below)
width: 320
# Required: Object detection model input height (default: shown below)
height: 320
# Required: Object detection model input colorspace
# Valid values are rgb, bgr, or yuv. (default: shown below)
input_pixel_format: rgb
# Required: Object detection model input tensor format
# Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
input_tensor: nhwc
# Optional: Data type of the model input tensor
# Valid values are float, float_denorm, or int (default: shown below)
input_dtype: int
# Required: Object detection model architecture, used by detectors that support more
# than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
# Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
model_type: ssd
# Required: Label name modifications. These are merged into the standard labelmap.
labelmap:
2: vehicle
# Optional: Map of object labels to their attribute labels (default: depends on model)
attributes_map:
person:
- amazon
- face
car:
- amazon
- fedex
- license_plate
- ups
# Optional: Audio Events Configuration
# NOTE: Can be overridden at the camera level
@@ -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)
docs/docs/configuration/advanced/system.md
+17 -15
View File
@@ -192,12 +192,13 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and open
```yaml
# Optional: model config
model:
path: /path/to/model
width: 320
height: 320
input_tensor: "nhwc"
input_pixel_format: "bgr"
models:
default:
path: /path/to/model
width: 320
height: 320
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:
labelmap:
2: vehicle
3: vehicle
5: vehicle
7: vehicle
15: animal
16: animal
17: animal
models:
default:
labelmap:
2: vehicle
3: vehicle
5: vehicle
7: vehicle
15: animal
16: animal
17: animal
```
Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
docs/docs/configuration/config.md
+8 -7
View File
@@ -334,13 +334,14 @@ detectors:
type: openvino
device: AUTO
model:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
models:
default:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
record:
enabled: True
docs/docs/configuration/object_detectors.md
+53 -15
View File
@@ -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:
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
width: 300 # width is in the model name as the first number in the "int"x"int" section
height: 300 # height is in the model name as the second number in the "int"x"int" section
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
models:
default:
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
width: 300 # width is in the model name as the first number in the "int"x"int" section
height: 300 # height is in the model name as the second number in the "int"x"int" section
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
```
#### Local Inference
@@ -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:
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
width: 300 # width is in the model name as the first number in the "int"x"int" section
height: 300 # height is in the model name as the second number in the "int"x"int" section
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
models:
default:
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
width: 300 # width is in the model name as the first number in the "int"x"int" section
height: 300 # height is in the model name as the second number in the "int"x"int" section
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
```
#### AI Hub Cloud Inference
@@ -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:
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
width: 300 # width is in the model name as the first number in the "int"x"int" section
height: 300 # height is in the model name as the second number in the "int"x"int" section
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
models:
default:
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
width: 300 # width is in the model name as the first number in the "int"x"int" section
height: 300 # height is in the model name as the second number in the "int"x"int" section
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
```
## AXERA
docs/docs/guides/getting_started.md
+8 -7
View File
@@ -228,13 +228,14 @@ detectors: # <---- add detectors
device: GPU
# We will use the default MobileNet_v2 model from OpenVINO.
model:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
models:
default:
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
cameras:
name_of_your_camera:
docs/docs/integrations/plus.md
+10 -8
View File
@@ -64,8 +64,9 @@ You can either choose the new model from the <NavPath path="Settings > System >
```yaml
detectors: ...
model:
path: plus://<your_model_id>
models:
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:
path: plus://<your_model_id>
labelmap:
3: animal
4: animal
5: animal
models:
default:
path: plus://<your_model_id>
labelmap:
3: animal
4: animal
5: animal
```
docs/docs/plus/first_model.md
+3 -2
View File
@@ -38,8 +38,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" />. In the *
```yaml
detectors: ...
model:
path: plus://<your_model_id>
models:
default:
path: plus://<your_model_id>
```
:::tip