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ultralytics 8.0.141 create new SettingsManager (#3790)

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docs/yolov5/tutorials/running_on_jetson_nano.md
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@ -6,7 +6,7 @@ keywords: TensorRT, NVIDIA Jetson, DeepStream SDK, deployment, Ultralytics, YOLO
# Deploy on NVIDIA Jetson using TensorRT and DeepStream SDK
📚 This guide explains how to deploy a trained model into NVIDIA Jetson Platform and perform inference using TensorRT and DeepStream SDK. Here we use TensorRT to maximize the inference performance on the Jetson platform.
📚 This guide explains how to deploy a trained model into NVIDIA Jetson Platform and perform inference using TensorRT and DeepStream SDK. Here we use TensorRT to maximize the inference performance on the Jetson platform.
UPDATED 18 November 2022.
## Hardware Verification
@ -117,7 +117,7 @@ pip3 install torch-1.10.0-cp36-cp36m-linux_aarch64.whl
sudo apt install -y libjpeg-dev zlib1g-dev
git clone --branch v0.11.1 https://github.com/pytorch/vision torchvision
cd torchvision
sudo python3 setup.py install
sudo python3 setup.py install
```
Here a list of the corresponding torchvision version that you need to install according to the PyTorch version:
@ -310,11 +310,11 @@ The following table summarizes how different models perform on **Jetson Xavier N
| Model Name | Precision | Inference Size | Inference Time (ms) | FPS |
|------------|-----------|----------------|---------------------|-----|
| YOLOv5s | FP32 | 320x320 | 16.66 | 60 |
| | FP32 | 640x640 | 33.33 | 30 |
| | INT8 | 640x640 | 16.66 | 60 |
| YOLOv5n | FP32 | 640x640 | 16.66 | 60 |
| YOLOv5s | FP32 | 320x320 | 16.66 | 60 |
| | FP32 | 640x640 | 33.33 | 30 |
| | INT8 | 640x640 | 16.66 | 60 |
| YOLOv5n | FP32 | 640x640 | 16.66 | 60 |
### Additional
This tutorial is written by our friends at seeed @lakshanthad and Elaine
This tutorial is written by our friends at seeed @lakshanthad and Elaine