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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)