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Tweaks (#23638)
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Browse Source 
* docs tweaks

* show reolink warning when using probe path in camera wizard

* note ffmpeg 8 default

* update links

* add faq about false positives

* tweak plus language

* Recommend OpenVINO uses YOLOv9 by default

* add mse/rtc live view faq

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
Josh Hawkins
2026-07-06 09:28:12 -06:00
committed by GitHub
co-authored by Nicolas Mowen
parent 279dcf9bca
commit 455b8687e8
22 changed files with 116 additions and 62 deletions
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docs/data/object_detectors_models.json
+9 -9
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@@ -44,22 +44,22 @@
"openvino": {
"title": "OpenVINO",
"models": [
{
"key": "ssd",
"label": "SSDLite MobileNet v2",
"recommended": true,
"download": "An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.",
"ui": "Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:\n\n| Field | Value |\n| ---------------------------------------- | ------------------------------------------ |\n| **Object detection model input width** | `300` |\n| **Object detection model input height** | `300` |\n| **Model Input Tensor Shape** | `nhwc` |\n| **Model Input Pixel Color Format** | `bgr` |\n| **Custom object detector model path** | `/openvino-model/ssdlite_mobilenet_v2.xml` |\n| **Label map for custom object detector** | `/openvino-model/coco_91cl_bkgr.txt` |",
"yaml": "detectors:\n ov:\n type: openvino\n device: GPU # Or NPU\n\nmodel:\n width: 300\n height: 300\n input_tensor: nhwc\n input_pixel_format: bgr\n path: /openvino-model/ssdlite_mobilenet_v2.xml\n labelmap_path: /openvino-model/coco_91cl_bkgr.txt"
},
{
"key": "yolov9",
"label": "YOLOv9",
"recommended": false,
"recommended": true,
"download": "YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).\n\n```sh\ndocker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'\nFROM python:3.11 AS build\nRUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*\nCOPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/\nWORKDIR /yolov9\nADD https://github.com/WongKinYiu/yolov9.git .\nRUN uv pip install --system -r requirements.txt\nRUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript\nARG MODEL_SIZE\nARG IMG_SIZE\nADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt\nRUN sed -i \"s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g\" models/experimental.py\nRUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx\nFROM scratch\nARG MODEL_SIZE\nARG IMG_SIZE\nCOPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx\nEOF\n```",
"ui": "Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:\n\n| Field | Value |\n| ---------------------------------------- | -------------------------------------------------------- |\n| **Object Detection Model Type** | `yolo-generic` |\n| **Object detection model input width** | `320` (should match the imgsize set during model export) |\n| **Object detection model input height** | `320` (should match the imgsize set during model export) |\n| **Model Input Tensor Shape** | `nchw` |\n| **Model Input D Type** | `float` |\n| **Custom object detector model path** | `/config/model_cache/yolo.onnx` |\n| **Label map for custom object detector** | `/labelmap/coco-80.txt` |",
"yaml": "detectors:\n ov:\n type: openvino\n device: GPU # or NPU\n\nmodel:\n model_type: yolo-generic\n width: 320 # <--- should match the imgsize set during model export\n height: 320 # <--- should match the imgsize set during model export\n input_tensor: nchw\n input_dtype: float\n path: /config/model_cache/yolo.onnx\n labelmap_path: /labelmap/coco-80.txt"
},
{
"key": "ssd",
"label": "SSDLite MobileNet v2",
"recommended": false,
"download": "An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.",
"ui": "Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:\n\n| Field | Value |\n| ---------------------------------------- | ------------------------------------------ |\n| **Object detection model input width** | `300` |\n| **Object detection model input height** | `300` |\n| **Model Input Tensor Shape** | `nhwc` |\n| **Model Input Pixel Color Format** | `bgr` |\n| **Custom object detector model path** | `/openvino-model/ssdlite_mobilenet_v2.xml` |\n| **Label map for custom object detector** | `/openvino-model/coco_91cl_bkgr.txt` |",
"yaml": "detectors:\n ov:\n type: openvino\n device: GPU # Or NPU\n\nmodel:\n width: 300\n height: 300\n input_tensor: nhwc\n input_pixel_format: bgr\n path: /openvino-model/ssdlite_mobilenet_v2.xml\n labelmap_path: /openvino-model/coco_91cl_bkgr.txt"
},
{
"key": "yolo-legacy",
"label": "YOLO (v3, v4, v7)",