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Refactor detector and model management (#23995)

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* Refactor detector and model management

* Fix model resolution field
This commit is contained in:
Nicolas Mowen
2026-08-22 11:40:42 -05:00
committed by Josh Hawkins
parent 7b42d94bfe
commit 5c9c02002f
63 changed files with 2052 additions and 1152 deletions
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docs/data/object_detectors_models.yaml
+411 -379
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@@ -7,10 +7,9 @@ edgeTPU:
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`.
ui: Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
yaml: |-
detectors:
coral:
type: edgetpu
device: usb
models:
- devices:
- edgetpu:usb
- key: yolov9
label: YOLOv9
recommended: false
@@ -29,17 +28,14 @@ edgeTPU:
| **Model Input D Type** | `int` (Frigate's default value) |
| **Object Detection Model Type** | `yolo-generic` |
yaml: |-
detectors:
coral:
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:
- devices:
- edgetpu:usb
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:
@@ -62,32 +58,29 @@ hailo8l:
The detector automatically selects the default model based on your hardware. Optionally, specify a local model path or URL to override.
yaml: |-
detectors:
hailo:
type: hailo8l
device: PCIe
models:
- devices:
- hailo8l:PCIe
width: 320
height: 320
input_tensor: nhwc
input_pixel_format: rgb
input_dtype: int
model_type: yolo-generic
labelmap_path: /labelmap/coco-80.txt
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
# 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
@@ -106,23 +99,20 @@ hailo8l:
Specify the local model path or URL for SSD MobileNet v1.
yaml: |-
detectors:
hailo:
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:
- devices:
- hailo8l:PCIe
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:
@@ -166,19 +156,16 @@ openvino:
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `yolo-generic` |
yaml: |-
detectors:
ov:
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:
- devices:
- openvino:GPU
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
@@ -197,18 +184,15 @@ openvino:
| **Model Input D Type** | `int` (Frigate's default value) |
| **Object Detection Model Type** | `ssd` (Frigate's default value) |
yaml: |-
detectors:
ov:
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:
- devices:
- openvino:GPU
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
@@ -235,19 +219,16 @@ openvino:
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `yolo-generic` |
yaml: |-
detectors:
ov:
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:
- devices:
- openvino:GPU
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
@@ -275,19 +256,16 @@ openvino:
| **Model Input D Type** | `int` (Frigate's default value) |
| **Object Detection Model Type** | `yolonas` |
yaml: |-
detectors:
ov:
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:
- devices:
- openvino:GPU
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
@@ -303,15 +281,12 @@ openvino:
| **Model Input D Type** | `int` (Frigate's default value) |
| **Object Detection Model Type** | `yolox` |
yaml: |-
detectors:
ov:
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:
- devices:
- openvino:GPU
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
@@ -345,18 +320,15 @@ openvino:
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `rfdetr` |
yaml: |-
detectors:
ov:
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:
- devices:
- openvino:GPU
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
@@ -443,19 +415,16 @@ openvino:
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `dfine` |
yaml: |-
detectors:
ov:
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:
- devices:
- openvino:CPU
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:
@@ -499,19 +468,16 @@ appleSilicon:
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `yolo-generic` |
yaml: |-
detectors:
apple-silicon:
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:
- devices:
- zmq:tcp://host.docker.internal:5555
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
@@ -538,19 +504,16 @@ appleSilicon:
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `yolo-generic` |
yaml: |-
detectors:
apple-silicon:
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:
- devices:
- zmq:tcp://host.docker.internal:5555
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:
@@ -594,18 +557,16 @@ 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
- key: rfdetr
label: RF-DETR
recommended: false
@@ -639,17 +600,15 @@ onnx:
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `rfdetr` |
yaml: |-
detectors:
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:
- 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)
docs/docs/configuration/advanced/reference.md
+52 -47
View File
@@ -56,17 +56,6 @@ mqtt:
# 2 = exactly once
qos: 0
# Optional: Detectors configuration. Defaults to a single CPU detector
detectors:
# Required: name of the detector
detector_name:
# Required: type of the detector
# Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
# Additional detector types can also be plugged in.
# Detectors may require additional configuration.
# Refer to the Detectors configuration page for more information.
type: cpu
# Optional: Database configuration
database:
# The path to store the SQLite DB (default: shown below)
@@ -157,44 +146,56 @@ auth:
- front_door
- back_yard
# Optional: model modifications
# Optional: object detection models. Defaults to a single model on a CPU detector.
# 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:
# Optional: the camera environment this model is for (default: shown below)
# Cameras select a model by setting detect -> scene to a matching value, and
# a model with a scene of all is used by any camera that does not set one.
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
- scene: all
# Required: hardware this model runs on, as <detector> or <detector>:<device>
# See https://docs.frigate.video/configuration/object_detectors for the
# detectors available and the devices each one accepts. All of a model's
# devices must use the same detector. Listing the same device more than once
# runs additional inference processes on it.
devices:
- edgetpu:pci:0
# 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
@@ -314,6 +315,10 @@ detect:
width: 1280
# Optional: height of the frame for the input with the detect role (default: use native stream resolution)
height: 720
# Optional: the environment this camera looks at, which picks the model it runs on
# (default: the model with a scene of all)
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
scene: outdoor
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
fps: 5
docs/docs/configuration/advanced/system.md
+17 -15
View File
@@ -192,12 +192,14 @@ 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:
- devices:
- openvino:GPU
path: /path/to/model
width: 320
height: 320
input_tensor: "nhwc"
input_pixel_format: "bgr"
```
</TabItem>
@@ -214,15 +216,15 @@ 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:
- 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
+12 -17
View File
@@ -172,10 +172,9 @@ mqtt:
ffmpeg:
hwaccel_args: preset-rpi-64-h264
detectors:
coral:
type: edgetpu
device: usb
models:
- devices:
- edgetpu:usb
record:
enabled: True
@@ -249,10 +248,9 @@ mqtt:
ffmpeg:
hwaccel_args: preset-vaapi
detectors:
coral:
type: edgetpu
device: usb
models:
- devices:
- edgetpu:usb
record:
enabled: True
@@ -329,15 +327,12 @@ mqtt:
ffmpeg:
hwaccel_args: preset-vaapi
detectors:
ov:
type: openvino
device: AUTO
model:
width: 300
height: 300
input_tensor: nhwc
models:
- devices:
- openvino:AUTO
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
docs/docs/configuration/object_detectors.md
+201 -59
View File
@@ -68,12 +68,66 @@ Frigate supports multiple different detectors that work on different types of ha
:::note
Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
A single model can not be spread across different detector types (ex: OpenVINO and Coral EdgeTPU can not run the same model at the same time). Configuring more than one model, each on its own detector type, is supported.
This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
:::
### Configuring models and hardware
Object detection is configured with a `models` list. Each entry describes one model and the hardware it runs on:
```yaml
models:
- devices:
- openvino:GPU
path: /config/model_cache/yolov9-s.onnx
model_type: yolo-generic
width: 320
height: 320
```
Each entry in `devices` is a detector type, optionally followed by a colon and a device for that detector, such as `edgetpu:pci:0`, `openvino:NPU`, or `tensorrt:0`. The per-detector sections below document the device values each one accepts. Listing several devices runs the model on all of them, and listing the **same** device more than once runs additional inference processes against it, which can improve throughput on hardware that keeps up with more than one stream:
```yaml
models:
- devices:
- openvino:GPU
- openvino:GPU
```
Coral EdgeTPU and MemryX accelerators can only be opened by one process, so those devices can not be repeated.
### Running more than one model
Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
```yaml
models:
- scene: outdoor
path: plus://your-outdoor-model
devices:
- edgetpu:pci:0
- scene: indoor
path: /config/model_cache/indoor.onnx
model_type: yolo-generic
devices:
- openvino:GPU
cameras:
driveway:
detect:
scene: outdoor
...
hallway:
detect:
scene: indoor
...
```
Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
### Choosing a model size
Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
@@ -92,11 +146,11 @@ The best detection accuracy comes from a model trained on images that look like
# 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.
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. Each of a model's devices runs in a dedicated process, and they pull from a common queue of detection requests from the cameras assigned to that model.
## Edge TPU Detector
The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To use it, prefix a model's device with `edgetpu`.
The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
@@ -117,10 +171,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
<TabItem value="yaml">
```yaml
detectors:
coral:
type: edgetpu
device: usb
models:
- devices:
- edgetpu:usb
```
</TabItem>
@@ -137,13 +190,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
<TabItem value="yaml">
```yaml
detectors:
coral1:
type: edgetpu
device: usb:0
coral2:
type: edgetpu
device: usb:1
models:
- devices:
- edgetpu:usb:0
- edgetpu:usb:1
```
</TabItem>
@@ -162,10 +212,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
<TabItem value="yaml">
```yaml
detectors:
coral:
type: edgetpu
device: ""
models:
- devices:
- 'edgetpu:'
```
</TabItem>
@@ -182,10 +231,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
<TabItem value="yaml">
```yaml
detectors:
coral:
type: edgetpu
device: pci
models:
- devices:
- edgetpu:pci
```
</TabItem>
@@ -202,13 +250,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
<TabItem value="yaml">
```yaml
detectors:
coral1:
type: edgetpu
device: pci:0
coral2:
type: edgetpu
device: pci:1
models:
- devices:
- edgetpu:pci:0
- edgetpu:pci:1
```
</TabItem>
@@ -225,13 +270,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
<TabItem value="yaml">
```yaml
detectors:
coral_usb:
type: edgetpu
device: usb
coral_pci:
type: edgetpu
device: pci
models:
- devices:
- edgetpu:usb
- edgetpu:pci
```
</TabItem>
@@ -273,7 +315,7 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc
## OpenVINO Detector
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To use it, prefix a model's device with `openvino`.
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.
@@ -286,13 +328,10 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
```yaml
detectors:
ov_0:
type: openvino
device: GPU # or NPU
ov_1:
type: openvino
device: GPU # or NPU
models:
- devices:
- openvino:GPU # or NPU
- openvino:GPU # or NPU
```
:::
@@ -313,6 +352,12 @@ Intel NPUs cannot be used under Home Assistant OS, which does not include the NP
## Apple Silicon detector
:::warning
The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
:::
The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
### Setup {#setup-apple-silicon}
@@ -453,11 +498,10 @@ If the correct build is used for your GPU then the GPU will be detected and used
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
```yaml
detectors:
onnx_0:
type: onnx
onnx_1:
type: onnx
models:
- devices:
- onnx
- onnx
```
:::
@@ -470,7 +514,7 @@ detectors:
## CPU Detector (not recommended)
The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To use it, set a model's device to `cpu`.
:::danger
@@ -480,7 +524,7 @@ The CPU detector is not recommended for general use. If you do not have GPU or E
The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
A TensorFlow Lite model is provided in the container at `/cpu_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`.
A TensorFlow Lite model is provided in the container at `/cpu_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 the model's `path`.
### Configuration {#configuration-cpu}
@@ -490,6 +534,12 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
## Deepstack / CodeProject.AI Server Detector
:::warning
The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
:::
The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
### Setup {#setup-deepstack}
@@ -552,7 +602,7 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
4. Bind-mount the `.zip` file into the container and specify its path using the model's `path` in your config.
5. Update `labelmap_path` to match your custom model's labels.
@@ -682,13 +732,10 @@ If no custom model is provided, the RKNN detector downloads a default model from
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
```yaml
detectors:
rknn_0:
type: rknn
num_cores: 0
rknn_1:
type: rknn
num_cores: 0
models:
- devices:
- rknn:0
- rknn:0
```
:::
@@ -762,6 +809,101 @@ Explanation of the parameters:
- **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
<<<<<<< HEAD
=======
## DeGirum
:::warning
The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
:::
DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
### Configuration {#configuration-degirum}
#### AI Server Inference
Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
```yaml
degirum_detector:
container_name: degirum
image: degirum/aiserver:latest
privileged: true
ports:
- "8778:8778"
```
All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
Once completed, configure the detector as follows:
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
The model is set on the same `models` entry as the DeGirum device. You can set it to:
- A model listed on the [AI Hub](https://hub.degirum.com)
- If this is what you choose to do, the correct model will be downloaded onto your machine before running.
- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
- A path to some model.json.
```yaml
models:
- devices:
- degirum:<location>
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
It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
3. Create a DeGirum detector in your configuration.
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
```yaml
models:
- devices:
- degirum:<location>
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
If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
2. Get an access token.
3. Create a DeGirum detector in your configuration.
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
```yaml
models:
- devices:
- degirum:<location>
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
```
>>>>>>> 34363affa (Refactor detector and model management)
## AXERA
Hardware accelerated object detection is supported on the following SoCs:
docs/docs/guides/getting_started.md
+13 -18
View File
@@ -222,15 +222,12 @@ You need to refer to **Configure hardware acceleration** above to enable the con
```yaml {3-6,9-15,20-21}
mqtt: ...
detectors: # <---- add detectors
ov:
type: openvino # <---- use openvino detector
device: GPU
# We will use the default MobileNet_v2 model from OpenVINO.
model:
width: 300
height: 300
models: # <---- add models
- devices:
- openvino:GPU # <---- use the openvino detector on the GPU
# We will use the default MobileNet_v2 model from OpenVINO.
width: 300
height: 300
input_tensor: nhwc
input_pixel_format: bgr
path: /openvino-model/ssdlite_mobilenet_v2.xml
@@ -281,10 +278,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a
```yaml {3-6,11-12}
mqtt: ...
detectors: # <---- add detectors
coral:
type: edgetpu
device: usb
models: # <---- add models
- devices:
- edgetpu:usb
cameras:
name_of_your_camera:
@@ -321,10 +317,9 @@ If you are using YAML to configure Frigate instead of the UI, your configuration
mqtt:
enabled: False
detectors:
coral:
type: edgetpu
device: usb
models:
- devices:
- edgetpu:usb
cameras:
name_of_your_camera:
@@ -357,7 +352,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
```yaml {16-17}
mqtt: ...
detectors: ...
models: ...
cameras:
name_of_your_camera:
docs/docs/integrations/plus.md
+10 -10
View File
@@ -62,10 +62,9 @@ Once you have [requested your first model](../plus/first_model.md) and gotten yo
You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
```yaml
detectors: ...
model:
path: plus://<your_model_id>
models:
- devices: ...
path: plus://<your_model_id>
```
:::note
@@ -79,10 +78,11 @@ 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:
- devices: ...
path: plus://<your_model_id>
labelmap:
3: animal
4: animal
5: animal
```
docs/docs/plus/first_model.md
+3 -4
View File
@@ -36,10 +36,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" />. In the *
<TabItem value="yaml">
```yaml
detectors: ...
model:
path: plus://<your_model_id>
models:
- devices: ...
path: plus://<your_model_id>
```
:::tip