update docs

docs/docs/configuration/object_detectors.md

@@ -113,6 +113,10 @@ The OpenVINO device to be used is specified using the `"device"` attribute accor
 
 OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. The `MYRIAD` device may be run on any platform, including Arm devices. For detailed system requirements, see [OpenVINO System Requirements](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
 
+### Supported Models
+
+#### SSDLite MobileNet v2
+
 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. Use the model configuration shown below when using the OpenVINO detector with the default model.
 
 ```yaml
@@ -131,7 +135,9 @@ model:
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 ```
 
-This detector also supports YOLOX. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/reference.md) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
+#### YOLOX
+
+This detector also supports YOLOX. Frigate does not come with any YOLOX models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/reference.md) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
 
 ```yaml
 detectors:
@@ -150,34 +156,25 @@ model:
   labelmap_path: /path/to/coco_80cl.txt
 ```
 
-### Intel NCS2 VPU and Myriad X Setup
+#### YOLO-NAS
 
-Intel produces a neural net inference acceleration chip called Myriad X. This chip was sold in their Neural Compute Stick 2 (NCS2) which has been discontinued. If intending to use the MYRIAD device for acceleration, additional setup is required to pass through the USB device. The host needs a udev rule installed to handle the NCS2 device.
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb)[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
 
-```bash
+After placing the downloaded onnx model in your config folder, you can use the following configuration:
-sudo usermod -a -G users "$(whoami)"
-cat <<EOF > 97-myriad-usbboot.rules
-SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
-SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
-EOF
-sudo cp 97-myriad-usbboot.rules /etc/udev/rules.d/
-sudo udevadm control --reload-rules
-sudo udevadm trigger
-```
 
-Additionally, the Frigate docker container needs to run with the following configuration:
+```yaml
+detectors:
+  ov:
+    type: openvino
+    device: AUTO
 
-```bash
+model:
---device-cgroup-rule='c 189:\* rmw' -v /dev/bus/usb:/dev/bus/usb
+  model_type: yolonas
-```
+  path: /config/yolo_nas_s.onnx
-
+  width: 320
-or in your compose file:
+  height: 320
-
+  input_tensor: nchw
-```yml
+  input_pixel_format: bgr
-device_cgroup_rules:
-  - "c 189:* rmw"
-volumes:
-  - /dev/bus/usb:/dev/bus/usb
 ```
 
 ## NVidia TensorRT Detector
@@ -362,7 +359,7 @@ The correct labelmap must be loaded for each model. If you use a custom model (s
 
 :::warning
 
-yolo-nas models use weights from DeciAI. These weights are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html 
+yolo-nas models use weights from DeciAI. These weights are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
 
 :::