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YOLOv8 Pose Estimation on reComputer R1000 with Hailo-8L

Introduction​

YOLOv8 (You Only Look Once version 8) is the popular most YOLO series of real-time pose estimation models. It builds upon the strengths of its predecessors by introducing several advancements in speed, accuracy, and flexibility. The Raspberry-pi-AI-kit is used to accelerate inference speed, featuring a 13 tera-operations per second (TOPS) neural network inference accelerator built around the Hailo-8L chip.

This wiki demonstrates pose estimation using YOLOv8 on reComputer R1000 with and without Raspberry-pi-AI-kit acceleration. The Raspberry Pi AI Kit enhances the performance of the Raspberry Pi and unlock its potential in artificial intelligence and machine learning applications, like smart retail, smart traffic and more. Although the Raspberry AI Kit is designed for Raspberry Pi 5, we have experimented it on our CM4-powered edge gateway. Excited about turning our edge device into an intelligent IoT gateway!

Prepare Hardware​

reComputer r1000Raspberry Pi AI Kit
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Run this project​

Step 1: Install AI kit​

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Step 2: Update system & Set pcie to gen3​

Updata system​

Open terminal on the reCompuer R1000, and input command as follows to update your system.

sudo apt update
sudo apt full-upgrade

Set pcie to gen3​

Open terminal on the reCompuer R1000, and input command as follows to config reCompuer R1000.

sudo raspi-config

Select option "6 Advanced Options"

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Then select option "A8 PCIe Speed"

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Choose "Yes" to enable PCIe Gen 3 mode

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Click "Finish" to exit

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Step 3: Install Hailo Software & Verify Installation​

Install Hailo Software​

Open terminal on the reCompuer R1000, and input command as follows to install Hailo software.

sudo apt install hailo-all
sudo reboot

Check Software and Hardware​

Open terminal on the reCompuer R1000, and input command as follows to check if hailo-all have been installed.

hailortcli fw-control identify

The right result show as bellow:

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Open terminal on the reCompuer R1000, and input command as follows to check if hailo-8L have been connected.

lspci | grep Hailo

The right result show as bellow:

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Run YOLOv8​

Open terminal on the reCompuer R1000, and input command as follows to run YOLOv8.

git clone https://github.com/Seeed-Projects/Benchmarking-YOLOv8-on-Raspberry-PI-reComputer-r1000-and-AIkit-Hailo-8L.git
cd Benchmarking-YOLOv8-on-Raspberry-PI-reComputer-r1000-and-AIkit-Hailo-8L
bash ./run.sh pose-estimation-hailo

Result​

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Result​

We compared the inference speed of YOLOv8 for pose estimation with input 640*640 resolution before and after acceleration using the AI kit. The results show that prior to acceleration, the inference speed was only 0.5 FPS, whereas after acceleration, it reached 27 FPS.

Project Outlook​

In this project, we benchmark the running speed of YOLOv8 on pose estimation with and without AI kit. The result shows that the AI kit can greatly improve the performance of the edge device. And in the future, we will benchmark the running speed of YOLOv8 in different scenarios.

Tech Support & Product Discussion​

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