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ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)

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docs/yolov5/tutorials/running_on_jetson_nano.md
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@ -1,11 +1,12 @@
---
comments: true
description: Deploy YOLOv5 on NVIDIA Jetson using TensorRT and DeepStream SDK for high performance inference. Step-by-step guide with code snippets.
---
# 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.
UPDATED 18 November 2022.
UPDATED 18 November 2022.
## Hardware Verification
@ -27,8 +28,7 @@ There are two major installation methods including,
You can find a very detailed installation guide from NVIDIA [official website](https://developer.nvidia.com/jetpack-sdk-461). You can also find guides corresponding to the above-mentioned [reComputer J1010](https://wiki.seeedstudio.com/reComputer_J1010_J101_Flash_Jetpack) and [reComputer J2021](https://wiki.seeedstudio.com/reComputer_J2021_J202_Flash_Jetpack).
## Install Necessary Packages
## Install Necessary Packages
- **Step 1.** Access the terminal of Jetson device, install pip and upgrade it
@ -89,7 +89,7 @@ Supported by JetPack 4.4 (L4T R32.4.3) / JetPack 4.4.1 (L4T R32.4.4) / JetPack 4
**PyTorch v1.12.0**
Supported by JetPack 5.0 (L4T R34.1.0) / JetPack 5.0.1 (L4T R34.1.1) / JetPack 5.0.2 (L4T R35.1.0) with Python 3.8
Supported by JetPack 5.0 (L4T R34.1.0) / JetPack 5.0.1 (L4T R34.1.1) / JetPack 5.0.2 (L4T R35.1.0) with Python 3.8
**file_name:** torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl
**URL:** [https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl](https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl)
@ -133,7 +133,7 @@ cd ~
git clone https://github.com/marcoslucianops/DeepStream-Yolo
```
- **Step 2.** Copy **gen_wts_yoloV5.py** from **DeepStream-Yolo/utils** into **yolov5** directory
- **Step 2.** Copy **gen_wts_yoloV5.py** from **DeepStream-Yolo/utils** into **yolov5** directory
```sh
cp DeepStream-Yolo/utils/gen_wts_yoloV5.py yolov5
@ -222,7 +222,7 @@ The above result is running on **Jetson Xavier NX** with **FP32** and **YOLOv5s
## INT8 Calibration
If you want to use INT8 precision for inference, you need to follow the steps below
If you want to use INT8 precision for inference, you need to follow the steps below
- **Step 1.** Install OpenCV
@ -254,7 +254,7 @@ for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do \
done
```
**Note:** NVIDIA recommends at least 500 images to get a good accuracy. On this example, 1000 images are chosen to get better accuracy (more images = more accuracy). Higher INT8_CALIB_BATCH_SIZE values will result in more accuracy and faster calibration speed. Set it according to you GPU memory. You can set it from **head -1000**. For example, for 2000 images, **head -2000**. This process can take a long time.
**Note:** NVIDIA recommends at least 500 images to get a good accuracy. On this example, 1000 images are chosen to get better accuracy (more images = more accuracy). Higher INT8_CALIB_BATCH_SIZE values will result in more accuracy and faster calibration speed. Set it according to you GPU memory. You can set it from **head -1000**. For example, for 2000 images, **head -2000**. This process can take a long time.
- **Step 6.** Create the **calibration.txt** file with all selected images
@ -305,7 +305,7 @@ The above result is running on **Jetson Xavier NX** with **INT8** and **YOLOv5s
## Benchmark results
The following table summarizes how different models perform on **Jetson Xavier NX**.
The following table summarizes how different models perform on **Jetson Xavier NX**.
| Model Name | Precision | Inference Size | Inference Time (ms) | FPS |
|------------|-----------|----------------|---------------------|-----|
@ -314,7 +314,6 @@ The following table summarizes how different models perform on **Jetson Xavier N
| | 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