mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-09-26 15:18:58 +03:00
Refactor detector and model management (#23995)
* Refactor detector and model management * Fix model resolution field
This commit is contained in:
Nicolas Mowen
@@ -7,10 +7,9 @@ edgeTPU:
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download: A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
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ui: Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
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yaml: |-
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detectors:
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coral:
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type: edgetpu
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device: usb
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models:
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- devices:
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- edgetpu:usb
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- key: yolov9
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label: YOLOv9
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recommended: false
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@@ -29,17 +28,14 @@ edgeTPU:
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| **Model Input D Type** | `int` (Frigate's default value) |
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| **Object Detection Model Type** | `yolo-generic` |
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yaml: |-
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detectors:
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coral:
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type: edgetpu
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device: usb
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model:
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize of the model, typically 320
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height: 320 # <--- should match the imgsize of the model, typically 320
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path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
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labelmap_path: /config/labels-coco17.txt
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models:
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- devices:
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- edgetpu:usb
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize of the model, typically 320
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height: 320 # <--- should match the imgsize of the model, typically 320
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path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
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labelmap_path: /config/labels-coco17.txt
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hailo8l:
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title: Hailo-8/Hailo-8L
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models:
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@@ -62,32 +58,29 @@ hailo8l:
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The detector automatically selects the default model based on your hardware. Optionally, specify a local model path or URL to override.
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yaml: |-
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detectors:
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hailo:
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type: hailo8l
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device: PCIe
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models:
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- devices:
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- hailo8l:PCIe
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width: 320
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height: 320
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input_tensor: nhwc
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input_pixel_format: rgb
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input_dtype: int
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model_type: yolo-generic
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labelmap_path: /labelmap/coco-80.txt
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model:
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width: 320
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height: 320
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input_tensor: nhwc
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input_pixel_format: rgb
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input_dtype: int
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model_type: yolo-generic
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labelmap_path: /labelmap/coco-80.txt
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# The detector automatically selects the default model based on your hardware:
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# - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
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# - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
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#
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# Optionally, you can specify a local model path to override the default.
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# If a local path is provided and the file exists, it will be used instead of downloading.
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# Example:
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# path: /config/model_cache/hailo/yolov6n.hef
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#
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# You can also override using a custom URL:
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# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
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# just make sure to give it the write configuration based on the model
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# The detector automatically selects the default model based on your hardware:
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# - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
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# - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
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#
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# Optionally, you can specify a local model path to override the default.
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# If a local path is provided and the file exists, it will be used instead of downloading.
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# Example:
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# path: /config/model_cache/hailo/yolov6n.hef
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#
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# You can also override using a custom URL:
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# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
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# just make sure to give it the write configuration based on the model
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- key: ssd
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label: SSD MobileNet v1
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recommended: false
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@@ -106,23 +99,20 @@ hailo8l:
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Specify the local model path or URL for SSD MobileNet v1.
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yaml: |-
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detectors:
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hailo:
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type: hailo8l
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device: PCIe
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model:
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width: 300
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height: 300
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input_tensor: nhwc
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input_pixel_format: rgb
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model_type: ssd
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# Specify the local model path (if available) or URL for SSD MobileNet v1.
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# Example with a local path:
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# path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
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#
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# Or override using a custom URL:
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# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
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models:
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- devices:
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- hailo8l:PCIe
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width: 300
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height: 300
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input_tensor: nhwc
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input_pixel_format: rgb
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model_type: ssd
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# Specify the local model path (if available) or URL for SSD MobileNet v1.
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# Example with a local path:
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# path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
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#
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# Or override using a custom URL:
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# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
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openvino:
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title: OpenVINO
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models:
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@@ -166,19 +156,16 @@ openvino:
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| **Model Input D Type** | `float` |
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| **Object Detection Model Type** | `yolo-generic` |
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yaml: |-
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detectors:
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ov:
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type: openvino
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device: GPU # or NPU
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model:
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize set during model export
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height: 320 # <--- should match the imgsize set during model export
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/yolo.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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models:
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- devices:
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- openvino:GPU
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize set during model export
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height: 320 # <--- should match the imgsize set during model export
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/yolo.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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- key: ssd
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label: SSDLite MobileNet v2
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recommended: false
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@@ -197,18 +184,15 @@ openvino:
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| **Model Input D Type** | `int` (Frigate's default value) |
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| **Object Detection Model Type** | `ssd` (Frigate's default value) |
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yaml: |-
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detectors:
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ov:
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type: openvino
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device: GPU # Or NPU
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model:
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width: 300
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height: 300
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input_tensor: nhwc
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input_pixel_format: bgr
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path: /openvino-model/ssdlite_mobilenet_v2.xml
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labelmap_path: /openvino-model/coco_91cl_bkgr.txt
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models:
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- devices:
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- openvino:GPU
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width: 300
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height: 300
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input_tensor: nhwc
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input_pixel_format: bgr
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path: /openvino-model/ssdlite_mobilenet_v2.xml
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labelmap_path: /openvino-model/coco_91cl_bkgr.txt
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- key: yolo-legacy
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label: YOLO (v3, v4, v7)
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recommended: false
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@@ -235,19 +219,16 @@ openvino:
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| **Model Input D Type** | `float` |
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| **Object Detection Model Type** | `yolo-generic` |
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yaml: |-
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detectors:
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ov:
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type: openvino
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device: GPU # or NPU
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model:
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize set during model export
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height: 320 # <--- should match the imgsize set during model export
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/yolo.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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models:
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- devices:
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- openvino:GPU
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize set during model export
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height: 320 # <--- should match the imgsize set during model export
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/yolo.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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- key: yolonas
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label: YOLO-NAS
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recommended: false
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@@ -275,19 +256,16 @@ openvino:
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| **Model Input D Type** | `int` (Frigate's default value) |
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| **Object Detection Model Type** | `yolonas` |
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yaml: |-
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detectors:
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ov:
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type: openvino
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device: GPU
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model:
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model_type: yolonas
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width: 320 # <--- should match whatever was set in notebook
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height: 320 # <--- should match whatever was set in notebook
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input_tensor: nchw
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input_pixel_format: bgr
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path: /config/yolo_nas_s.onnx
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labelmap_path: /labelmap/coco-80.txt
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models:
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- devices:
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- openvino:GPU
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model_type: yolonas
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width: 320 # <--- should match whatever was set in notebook
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height: 320 # <--- should match whatever was set in notebook
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input_tensor: nchw
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input_pixel_format: bgr
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path: /config/yolo_nas_s.onnx
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labelmap_path: /labelmap/coco-80.txt
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- key: yolox
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label: YOLOX
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recommended: false
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@@ -303,15 +281,12 @@ openvino:
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| **Model Input D Type** | `int` (Frigate's default value) |
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| **Object Detection Model Type** | `yolox` |
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yaml: |-
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detectors:
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ov:
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type: openvino
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device: GPU
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model:
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model_type: yolox
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path: /config/model_cache/yolox.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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models:
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- devices:
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- openvino:GPU
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model_type: yolox
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path: /config/model_cache/yolox.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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- key: rfdetr
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label: RF-DETR
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recommended: false
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@@ -345,18 +320,15 @@ openvino:
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| **Model Input D Type** | `float` |
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| **Object Detection Model Type** | `rfdetr` |
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yaml: |-
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detectors:
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ov:
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type: openvino
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device: GPU
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model:
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model_type: rfdetr
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width: 320
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height: 320
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/rfdetr.onnx # use the filename you generated above
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models:
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- devices:
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- openvino:GPU
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model_type: rfdetr
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width: 320
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height: 320
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/rfdetr.onnx # use the filename you generated above
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- key: dfine
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label: D-FINE / DEIMv2
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recommended: false
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@@ -443,19 +415,16 @@ openvino:
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| **Model Input D Type** | `float` |
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| **Object Detection Model Type** | `dfine` |
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yaml: |-
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detectors:
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ov:
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type: openvino
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device: CPU
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model:
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model_type: dfine
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width: 640
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height: 640
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/dfine-s.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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models:
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- devices:
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- openvino:CPU
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model_type: dfine
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width: 640
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height: 640
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input_tensor: nchw
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input_dtype: float
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path: /config/model_cache/dfine-s.onnx # use the filename you generated above
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labelmap_path: /labelmap/coco-80.txt
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appleSilicon:
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title: Apple Silicon
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models:
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@@ -499,19 +468,16 @@ appleSilicon:
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| **Model Input D Type** | `float` |
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| **Object Detection Model Type** | `yolo-generic` |
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yaml: |-
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detectors:
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apple-silicon:
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type: zmq
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endpoint: tcp://host.docker.internal:5555
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model:
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize set during model export
|
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height: 320 # <--- should match the imgsize set during model export
|
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input_tensor: nchw
|
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input_dtype: float
|
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path: /config/model_cache/yolo.onnx # use the filename you generated above
|
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labelmap_path: /labelmap/coco-80.txt
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models:
|
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- devices:
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- zmq:tcp://host.docker.internal:5555
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model_type: yolo-generic
|
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width: 320 # <--- should match the imgsize set during model export
|
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height: 320 # <--- should match the imgsize set during model export
|
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input_tensor: nchw
|
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input_dtype: float
|
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path: /config/model_cache/yolo.onnx # use the filename you generated above
|
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labelmap_path: /labelmap/coco-80.txt
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- key: yolo-legacy
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label: YOLO (v3, v4, v7)
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recommended: false
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@@ -538,19 +504,16 @@ appleSilicon:
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| **Model Input D Type** | `float` |
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| **Object Detection Model Type** | `yolo-generic` |
|
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yaml: |-
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detectors:
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apple-silicon:
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type: zmq
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endpoint: tcp://host.docker.internal:5555
|
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|
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model:
|
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model_type: yolo-generic
|
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width: 320 # <--- should match the imgsize set during model export
|
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height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
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input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
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labelmap_path: /labelmap/coco-80.txt
|
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models:
|
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- devices:
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- zmq:tcp://host.docker.internal:5555
|
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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
|
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labelmap_path: /labelmap/coco-80.txt
|
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onnx:
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title: ONNX
|
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models:
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@@ -594,18 +557,16 @@ onnx:
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| **Model Input D Type** | `float` |
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| **Object Detection Model Type** | `yolo-generic` |
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yaml: |-
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detectors:
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onnx:
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type: onnx
|
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|
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model:
|
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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
|
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labelmap_path: /labelmap/coco-80.txt
|
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models:
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- devices:
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- onnx
|
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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
|
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label: RF-DETR
|
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recommended: false
|
||||
@@ -639,17 +600,15 @@ onnx:
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| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `rfdetr` |
|
||||
yaml: |-
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||||
detectors:
|
||||
onnx:
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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:
|
||||
- devices:
|
||||
- onnx
|
||||
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
|
||||
@@ -677,18 +636,16 @@ onnx:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
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:
|
||||
- devices:
|
||||
- onnx
|
||||
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
|
||||
@@ -707,18 +664,16 @@ onnx:
|
||||
| **Model Input D Type** | `float_denorm` |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
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:
|
||||
- devices:
|
||||
- onnx
|
||||
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
|
||||
@@ -805,18 +760,16 @@ onnx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `dfine` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
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:
|
||||
- devices:
|
||||
- onnx
|
||||
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
|
||||
@@ -843,18 +796,16 @@ onnx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
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:
|
||||
- devices:
|
||||
- onnx
|
||||
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:
|
||||
@@ -870,10 +821,9 @@ cpu:
|
||||
| **Detector type** | `cpu` |
|
||||
| **Num threads** | `3` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
cpu1:
|
||||
type: cpu
|
||||
num_threads: 3
|
||||
models:
|
||||
- devices:
|
||||
- cpu:3
|
||||
deepstack:
|
||||
title: DeepStack / CodeProject.AI
|
||||
models:
|
||||
@@ -889,11 +839,9 @@ deepstack:
|
||||
| **API URL** | `http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection` |
|
||||
| **API Timeout** | `0.1` (seconds) |
|
||||
yaml: |-
|
||||
detectors:
|
||||
deepstack:
|
||||
api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
|
||||
type: deepstack
|
||||
api_timeout: 0.1 # seconds
|
||||
models:
|
||||
- devices:
|
||||
- deepstack:http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
|
||||
memryx:
|
||||
title: MemryX
|
||||
models:
|
||||
@@ -923,23 +871,20 @@ memryx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
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:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
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
|
||||
@@ -960,22 +905,19 @@ memryx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
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:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
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
|
||||
@@ -996,22 +938,19 @@ memryx:
|
||||
| **Model Input D Type** | `float_denorm` |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
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:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
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
|
||||
@@ -1032,23 +971,20 @@ memryx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `ssd` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
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:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
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:
|
||||
@@ -1082,18 +1018,15 @@ tensorrt:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `ssd` (Frigate's default value) |
|
||||
yaml: |-
|
||||
detectors:
|
||||
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:
|
||||
- devices:
|
||||
- tensorrt:0
|
||||
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:
|
||||
@@ -1115,16 +1048,15 @@ synaptics:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `ssd` (Frigate's default value) |
|
||||
yaml: |-
|
||||
detectors: # required
|
||||
synap_npu: # required
|
||||
type: synaptics # required
|
||||
|
||||
model: # required
|
||||
path: /synaptics/mobilenet.synap # required
|
||||
width: 224 # required
|
||||
height: 224 # required
|
||||
input_tensor: nhwc # default value (optional. If you change the model, it is required)
|
||||
labelmap_path: /labelmap/coco-80.txt # required
|
||||
models:
|
||||
- # required
|
||||
devices:
|
||||
- synaptics
|
||||
path: /synaptics/mobilenet.synap # required
|
||||
width: 224 # required
|
||||
height: 224 # required
|
||||
input_tensor: nhwc # default value (optional. If you change the model, it is required)
|
||||
labelmap_path: /labelmap/coco-80.txt # required
|
||||
rknn:
|
||||
title: RKNN
|
||||
models:
|
||||
@@ -1149,21 +1081,23 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - frigate-fp16-yolov9-t
|
||||
# - frigate-fp16-yolov9-s
|
||||
# - frigate-fp16-yolov9-m
|
||||
# - frigate-fp16-yolov9-c
|
||||
# - frigate-fp16-yolov9-e
|
||||
# your yolo_model.rknn
|
||||
path: frigate-fp16-yolov9-t
|
||||
model_type: yolo-generic
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- rknn
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - frigate-fp16-yolov9-t
|
||||
# - frigate-fp16-yolov9-s
|
||||
# - frigate-fp16-yolov9-m
|
||||
# - frigate-fp16-yolov9-c
|
||||
# - frigate-fp16-yolov9-e
|
||||
# your yolo_model.rknn
|
||||
path: frigate-fp16-yolov9-t
|
||||
model_type: yolo-generic
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolonas
|
||||
label: YOLO-NAS
|
||||
recommended: false
|
||||
@@ -1187,20 +1121,22 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - deci-fp16-yolonas_s
|
||||
# - deci-fp16-yolonas_m
|
||||
# - deci-fp16-yolonas_l
|
||||
# your yolonas_model.rknn
|
||||
path: deci-fp16-yolonas_s
|
||||
model_type: yolonas
|
||||
width: 320
|
||||
height: 320
|
||||
input_pixel_format: bgr
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- rknn
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - deci-fp16-yolonas_s
|
||||
# - deci-fp16-yolonas_m
|
||||
# - deci-fp16-yolonas_l
|
||||
# your yolonas_model.rknn
|
||||
path: deci-fp16-yolonas_s
|
||||
model_type: yolonas
|
||||
width: 320
|
||||
height: 320
|
||||
input_pixel_format: bgr
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolox
|
||||
label: YOLOx
|
||||
recommended: false
|
||||
@@ -1222,20 +1158,22 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - rock-i8-yolox_nano
|
||||
# - rock-i8-yolox_tiny
|
||||
# - rock-fp16-yolox_nano
|
||||
# - rock-fp16-yolox_tiny
|
||||
# your yolox_model.rknn
|
||||
path: rock-i8-yolox_nano
|
||||
model_type: yolox
|
||||
width: 416
|
||||
height: 416
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- rknn
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - rock-i8-yolox_nano
|
||||
# - rock-i8-yolox_tiny
|
||||
# - rock-fp16-yolox_nano
|
||||
# - rock-fp16-yolox_tiny
|
||||
# your yolox_model.rknn
|
||||
path: rock-i8-yolox_nano
|
||||
model_type: yolox
|
||||
width: 416
|
||||
height: 416
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
axengine:
|
||||
title: AXEngine
|
||||
models:
|
||||
@@ -1257,6 +1195,7 @@ axengine:
|
||||
| **Model Input D Type** | `int` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
<<<<<<< HEAD
|
||||
detectors:
|
||||
axengine:
|
||||
type: axengine
|
||||
@@ -1269,3 +1208,96 @@ axengine:
|
||||
input_dtype: int
|
||||
input_pixel_format: bgr
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
=======
|
||||
models:
|
||||
- devices:
|
||||
- axengine
|
||||
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:
|
||||
- key: ai-server-inference
|
||||
label: AI Server Inference
|
||||
recommended: true
|
||||
download: |-
|
||||
Launch a DeGirum AI server as a Docker container, then point the detector at it. Add this to your `docker-compose.yml`:
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
container_name: degirum
|
||||
image: degirum/aiserver:latest
|
||||
privileged: true
|
||||
ports:
|
||||
- "8778:8778"
|
||||
```
|
||||
|
||||
Set `location` to the server's service name, container name, or `host:port`.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `degirum` |
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
models:
|
||||
- devices:
|
||||
- degirum:degirum
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300
|
||||
height: 300
|
||||
input_pixel_format: rgb
|
||||
degirumLocal:
|
||||
title: DeGirum Local
|
||||
models:
|
||||
- key: local-inference
|
||||
label: Local Inference
|
||||
recommended: true
|
||||
download: Run hardware directly inside the Frigate container with `@local`, removing the AI server hop. The matching device runtime (e.g. the Hailo runtime) must be installed in the container; confirm it with `degirum sys-info`.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `@local` |
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
models:
|
||||
- devices:
|
||||
- degirum:@local
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300
|
||||
height: 300
|
||||
input_pixel_format: rgb
|
||||
degirumCloud:
|
||||
title: DeGirum AI Hub Cloud
|
||||
models:
|
||||
- key: ai-hub-cloud-inference
|
||||
label: AI Hub Cloud Inference
|
||||
recommended: true
|
||||
download: Run inferences on DeGirum's [AI Hub](https://hub.degirum.com) cloud with `@cloud`. Sign up, create an access token, and set it as `token`. Network latency may require lowering your detection fps.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `@cloud` |
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
models:
|
||||
- devices:
|
||||
- degirum:@cloud
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300
|
||||
height: 300
|
||||
input_pixel_format: rgb
|
||||
>>>>>>> 34363affa (Refactor detector and model management)
|
||||
|
||||
Reference in New Issue
Block a user