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docs: fix spelling mistakes in documentation

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TheInfamousToTo 2026-06-01 17:01:52 +00:00
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3 changed files with 18 additions and 18 deletions
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2
docs/docs/configuration/cameras.md
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@ -179,7 +179,7 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
| Hikvision DS-2DE3A404IWG-E/W | ✅ | ✅ | | | Hikvision DS-2DE3A404IWG-E/W | ✅ | ✅ | |
| Reolink | ✅ | ❌ | | | Reolink | ✅ | ❌ | |
| Speco O8P32X | ✅ | ❌ | | | Speco O8P32X | ✅ | ❌ | |
| Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable. | | Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatible. |
| Tapo | ✅ | ❌ | Many models supported, ONVIF Service Port: 2020 | | Tapo | ✅ | ❌ | Many models supported, ONVIF Service Port: 2020 |
| Uniview IPC672LR-AX4DUPK | ✅ | ❌ | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands | | Uniview IPC672LR-AX4DUPK | ✅ | ❌ | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands |
| Uniview IPC6612SR-X33-VG | ✅ | ✅ | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working. | | Uniview IPC6612SR-X33-VG | ✅ | ✅ | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working. |

30
docs/docs/configuration/object_detectors.md
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@ -660,7 +660,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
#### RF-DETR #### RF-DETR
[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate. [RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
:::warning :::warning
@ -732,12 +732,12 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
| Field | Value | | Field | Value |
| ---------------------------------------- | ---------------------------------- | | ---------------------------------------- | ---------------------------------- |
| **Object Detection Model Type** | `dfine` | | **Object Detection Model Type** | `define` |
| **Object detection model input width** | `640` | | **Object detection model input width** | `640` |
| **Object detection model input height** | `640` | | **Object detection model input height** | `640` |
| **Model Input Tensor Shape** | `nchw` | | **Model Input Tensor Shape** | `nchw` |
| **Model Input D Type** | `float` | | **Model Input D Type** | `float` |
| **Custom object detector model path** | `/config/model_cache/dfine-s.onnx` | | **Custom object detector model path** | `/config/model_cache/define-s.onnx` |
| **Label map for custom object detector** | `/labelmap/coco-80.txt` | | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
</TabItem> </TabItem>
@ -750,12 +750,12 @@ detectors:
device: CPU device: CPU
model: model:
model_type: dfine model_type: define
width: 640 width: 640
height: 640 height: 640
input_tensor: nchw input_tensor: nchw
input_dtype: float input_dtype: float
path: /config/model_cache/dfine-s.onnx path: /config/model_cache/define-s.onnx
labelmap_path: /labelmap/coco-80.txt labelmap_path: /labelmap/coco-80.txt
``` ```
@ -778,7 +778,7 @@ detectors:
device: CPU device: CPU
model: model:
model_type: dfine model_type: define
width: 640 width: 640
height: 640 height: 640
input_tensor: nchw input_tensor: nchw
@ -1256,12 +1256,12 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
| Field | Value | | Field | Value |
| ---------------------------------------- | ------------------------------------------- | | ---------------------------------------- | ------------------------------------------- |
| **Object Detection Model Type** | `dfine` | | **Object Detection Model Type** | `define` |
| **Object detection model input width** | `640` | | **Object detection model input width** | `640` |
| **Object detection model input height** | `640` | | **Object detection model input height** | `640` |
| **Model Input Tensor Shape** | `nchw` | | **Model Input Tensor Shape** | `nchw` |
| **Model Input D Type** | `float` | | **Model Input D Type** | `float` |
| **Custom object detector model path** | `/config/model_cache/dfine_m_obj2coco.onnx` | | **Custom object detector model path** | `/config/model_cache/define_m_obj2coco.onnx` |
| **Label map for custom object detector** | `/labelmap/coco-80.txt` | | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
</TabItem> </TabItem>
@ -1273,12 +1273,12 @@ detectors:
type: onnx type: onnx
model: model:
model_type: dfine model_type: define
width: 640 width: 640
height: 640 height: 640
input_tensor: nchw input_tensor: nchw
input_dtype: float input_dtype: float
path: /config/model_cache/dfine_m_obj2coco.onnx path: /config/model_cache/define_m_obj2coco.onnx
labelmap_path: /labelmap/coco-80.txt labelmap_path: /labelmap/coco-80.txt
``` ```
@ -1298,7 +1298,7 @@ detectors:
type: onnx type: onnx
model: model:
model_type: dfine model_type: define
width: 640 width: 640
height: 640 height: 640
input_tensor: nchw input_tensor: nchw
@ -2324,20 +2324,20 @@ docker build . --build-arg MODEL_SIZE=s --output . -f- <<'EOF'
FROM python:3.11 AS build FROM python:3.11 AS build
RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/* RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
WORKDIR /dfine WORKDIR /define
RUN git clone https://github.com/Peterande/D-FINE.git . RUN git clone https://github.com/Peterande/D-FINE.git .
RUN uv pip install --system -r requirements.txt RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx onnxruntime onnxsim onnxscript RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
# Create output directory and download checkpoint # Create output directory and download checkpoint
RUN mkdir -p output RUN mkdir -p output
ARG MODEL_SIZE ARG MODEL_SIZE
RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth RUN wget https://github.com/Peterande/storage/releases/download/definev1.0/define_${MODEL_SIZE}_obj2coco.pth -O output/define_${MODEL_SIZE}_obj2coco.pth
# Modify line 58 of export_onnx.py to change batch size to 1 # Modify line 58 of export_onnx.py to change batch size to 1
RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth RUN python3 tools/deployment/export_onnx.py -c configs/define/objects365/define_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/define_${MODEL_SIZE}_obj2coco.pth
FROM scratch FROM scratch
ARG MODEL_SIZE ARG MODEL_SIZE
COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx COPY --from=build /define/output/define_${MODEL_SIZE}_obj2coco.onnx /define-${MODEL_SIZE}.onnx
EOF EOF
``` ```

4
docs/docs/frigate/installation.md
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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
::: :::
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 negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application. 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.
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) 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)
@ -768,7 +768,7 @@ services:
- /path/to/your/recordings:/recordings - /path/to/your/recordings:/recordings
ports: ports:
- "8971:8971" - "8971:8971"
# If exposing on macOS map to a diffent host port like 5001 or any orher port with no conflicts # If exposing on macOS map to a different host port like 5001 or any orher port with no conflicts
# - "5001:5000" # Internal unauthenticated access. Expose carefully. # - "5001:5000" # Internal unauthenticated access. Expose carefully.
- "8554:8554" # RTSP feeds - "8554:8554" # RTSP feeds
extra_hosts: extra_hosts: