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docs: fix typos and revert incorrect dfine to define rename
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@ -732,12 +732,12 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
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| Field | Value |
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| Field | Value |
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| ---------------------------------------- | ---------------------------------- |
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| ---------------------------------------- | ---------------------------------- |
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| **Object Detection Model Type** | `define` |
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| **Object Detection Model Type** | `dfine` |
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| **Object detection model input width** | `640` |
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| **Object detection model input width** | `640` |
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| **Object detection model input height** | `640` |
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| **Object detection model input height** | `640` |
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| **Model Input Tensor Shape** | `nchw` |
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| **Model Input Tensor Shape** | `nchw` |
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| **Model Input D Type** | `float` |
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| **Model Input D Type** | `float` |
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| **Custom object detector model path** | `/config/model_cache/define-s.onnx` |
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| **Custom object detector model path** | `/config/model_cache/dfine-s.onnx` |
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| **Label map for custom object detector** | `/labelmap/coco-80.txt` |
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| **Label map for custom object detector** | `/labelmap/coco-80.txt` |
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</TabItem>
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</TabItem>
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@ -750,12 +750,12 @@ detectors:
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device: CPU
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device: CPU
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model:
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model:
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model_type: define
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model_type: dfine
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width: 640
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width: 640
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height: 640
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height: 640
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input_tensor: nchw
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input_tensor: nchw
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input_dtype: float
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input_dtype: float
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path: /config/model_cache/define-s.onnx
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path: /config/model_cache/dfine-s.onnx
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labelmap_path: /labelmap/coco-80.txt
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labelmap_path: /labelmap/coco-80.txt
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```
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```
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@ -778,7 +778,7 @@ detectors:
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device: CPU
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device: CPU
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model:
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model:
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model_type: define
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model_type: dfine
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width: 640
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width: 640
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height: 640
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height: 640
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input_tensor: nchw
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input_tensor: nchw
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@ -1256,12 +1256,12 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
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| Field | Value |
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| Field | Value |
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| ---------------------------------------- | ------------------------------------------- |
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| ---------------------------------------- | ------------------------------------------- |
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| **Object Detection Model Type** | `define` |
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| **Object Detection Model Type** | `dfine` |
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| **Object detection model input width** | `640` |
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| **Object detection model input width** | `640` |
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| **Object detection model input height** | `640` |
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| **Object detection model input height** | `640` |
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| **Model Input Tensor Shape** | `nchw` |
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| **Model Input Tensor Shape** | `nchw` |
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| **Model Input D Type** | `float` |
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| **Model Input D Type** | `float` |
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| **Custom object detector model path** | `/config/model_cache/define_m_obj2coco.onnx` |
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| **Custom object detector model path** | `/config/model_cache/dfine_m_obj2coco.onnx` |
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| **Label map for custom object detector** | `/labelmap/coco-80.txt` |
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| **Label map for custom object detector** | `/labelmap/coco-80.txt` |
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</TabItem>
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</TabItem>
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@ -1273,12 +1273,12 @@ detectors:
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type: onnx
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type: onnx
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model:
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model:
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model_type: define
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model_type: dfine
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width: 640
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width: 640
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height: 640
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height: 640
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input_tensor: nchw
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input_tensor: nchw
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input_dtype: float
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input_dtype: float
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path: /config/model_cache/define_m_obj2coco.onnx
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path: /config/model_cache/dfine_m_obj2coco.onnx
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labelmap_path: /labelmap/coco-80.txt
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labelmap_path: /labelmap/coco-80.txt
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```
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```
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@ -1298,7 +1298,7 @@ detectors:
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type: onnx
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type: onnx
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model:
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model:
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model_type: define
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model_type: dfine
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width: 640
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width: 640
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height: 640
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height: 640
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input_tensor: nchw
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input_tensor: nchw
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@ -2324,20 +2324,20 @@ docker build . --build-arg MODEL_SIZE=s --output . -f- <<'EOF'
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FROM python:3.11 AS build
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FROM python:3.11 AS build
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RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
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RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
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COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
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COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
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WORKDIR /define
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WORKDIR /dfine
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RUN git clone https://github.com/Peterande/D-FINE.git .
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RUN git clone https://github.com/Peterande/D-FINE.git .
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RUN uv pip install --system -r requirements.txt
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RUN uv pip install --system -r requirements.txt
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RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
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RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
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# Create output directory and download checkpoint
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# Create output directory and download checkpoint
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RUN mkdir -p output
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RUN mkdir -p output
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ARG MODEL_SIZE
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ARG MODEL_SIZE
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RUN wget https://github.com/Peterande/storage/releases/download/definev1.0/define_${MODEL_SIZE}_obj2coco.pth -O output/define_${MODEL_SIZE}_obj2coco.pth
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RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth
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# Modify line 58 of export_onnx.py to change batch size to 1
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# Modify line 58 of export_onnx.py to change batch size to 1
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RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
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RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
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RUN python3 tools/deployment/export_onnx.py -c configs/define/objects365/define_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/define_${MODEL_SIZE}_obj2coco.pth
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RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth
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FROM scratch
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FROM scratch
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ARG MODEL_SIZE
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ARG MODEL_SIZE
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COPY --from=build /define/output/define_${MODEL_SIZE}_obj2coco.onnx /define-${MODEL_SIZE}.onnx
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COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx
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EOF
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EOF
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```
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```
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@ -749,7 +749,7 @@ Failure to remap port 5000 on the host will result in the WebUI and all API endp
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:::
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:::
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Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligible, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
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Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native Swift app). The difference in inference speeds is negligible, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
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To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
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To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
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