From c17f83259f7f4ae4eaf51860147e9295b3189233 Mon Sep 17 00:00:00 2001 From: Blake Blackshear Date: Thu, 30 May 2024 07:27:41 -0500 Subject: [PATCH] improve docs --- docs/docs/configuration/object_detectors.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/docs/docs/configuration/object_detectors.md b/docs/docs/configuration/object_detectors.md index 99df61a6d..f46ae8de5 100644 --- a/docs/docs/configuration/object_detectors.md +++ b/docs/docs/configuration/object_detectors.md @@ -158,7 +158,15 @@ model: #### YOLO-NAS -[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). +[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 with pre-trained weights 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). + +:::warning + +The pre-trained YOLO-NAS weights from DeciAI 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 + +::: + +The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired. After placing the downloaded onnx model in your config folder, you can use the following configuration: @@ -171,8 +179,8 @@ detectors: model: model_type: yolonas path: /config/yolo_nas_s.onnx - width: 320 - height: 320 + 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 ``` @@ -359,7 +367,7 @@ 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 +The pre-trained YOLO-NAS weights from DeciAI 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 :::