如果你看到这个仓库，证明你想试试这个多线程的推理。

1. 这个里的代码不是最优的，有些错序的问题需要其他手段解决，我没有在这里解决，你可以看下一个标题新版本仓库的链接。新仓库解决了这个问题
2. 本仓库的代码思路想法，在我的B站上有详细的讲解，需要理解程序的可以去b站搜我“kaylordut”
3. 项目合作的可以发邮件到kaylor.chen@qq.com, 邮件请说明来意，和简单的需求，以及你的预算。邮件我一般都回复，请不要一来就索要微信，一个切实可行的项目或者良好的技术交流是良好的开始。

如果你缺一个运行改代码的开发板，访问这里
If you need a development board to run this code, visit here

New Project

An inference framework compatible with TensorRT, OnnxRuntime, NNRT and RKNN
If you want to find some more yolo8/yolo11 demo and depth anything demo, visit my another repository

Yolov8/v10 Demo for RK3588

The project is a multi-threaded inference demo of Yolov8 running on the RK3588 platform, which has been adapted for reading video files and camera feeds. The demo uses the Yolov8n model for file inference, with a maximum inference frame rate of up to 100 frames per second.

If you want to test yolov8n with ros2 for yourself kit, click the link

Model

Download Model File

you can find the model file in the 'src/yolov8/model', and some large files: Link: https://pan.baidu.com/s/1zfSVzR1G7mb-EQvs6A6ZYw?pwd=gmcs Password: gmcs
Google Drive: https://drive.google.com/drive/folders/1FYluJpdaL-680pipgIQ1zsqqRvNbruEp?usp=sharing

Model pt --> onnx

For Yolov8

go to my blog --> blog.kaylordut.com

For Yolov10

go to my another repository --> yolov10
download pt model and export:

# End-to-End ONNX
yolo export model=yolov10n/s/m/b/l/x.pt format=onnx opset=13 simplify

Model onnx --> rknn

go to my blog --> blog.kaylordut.com

TIPS: (Yolov10)

• rknn-toolkit2(release:1.6.0) does not support some operators about attention, so it runs attention steps with CPU, leading to increased inference time.
• rknn-toolkit2(beta:2.0.0b12) has the attention operators for 3588, so I build a docker image, you can pull it from kaylor/rknn_onnx2rknn:beta

Inference Time

Please refer to the spreadsheet '8vs10.xlsx' for details.

V8l-2.0.0	V8l-1.6.0	V10l-2.0.0	V10l-1.6.0	V8n-2.0.0	V8n-1.6.0	V10n-2.0.0	V10n-1.6.0
133.07572815534	133.834951456311	122.992233009709	204.471844660194	17.8990291262136	18.3300970873786	21.3009708737864	49.9883495145631

Demo Video and Guideline

https://space.bilibili.com/327258623?spm_id_from=333.999.0.0
QQ group1: 957577822 (full) QQ group2: 546943464

Prepare

Build the Cross-Compilation Environment

Set up a cross-compilation environment based on the following link.

Install Runtime Libraries in Your RK3588 Target Board

cat << 'EOF' | sudo tee /etc/apt/sources.list.d/kaylordut.list 
deb [signed-by=/etc/apt/keyrings/kaylor-keyring.gpg] http://apt.kaylordut.cn/kaylordut/ kaylordut main
EOF
sudo mkdir /etc/apt/keyrings -pv
sudo wget -O /etc/apt/keyrings/kaylor-keyring.gpg http://apt.kaylordut.cn/kaylor-keyring.gpg
sudo apt update
sudo apt install kaylordut-dev libbytetrack

If your OS is not Ubuntu22.04, and find kaylordut-dev and libbytetrack sources in my github.

Build the Project for Your RK3588

• Compile

git clone https://github.com/kaylorchen/rk3588-yolo-demo.git 
cd rk3588-yolo-demo/src/yolov8
mkdir build
cd build
cmake -DCMAKE_TOOLCHAIN_FILE=/path/to/toolchain-aarch64.cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON ..
make 

/path/to/toolchain-aarch64.cmake is .cmake file absolute path

• Run

Usage: ./videofile_demo [--model_path|-m model_path] [--input_filename|-i input_filename] [--threads|-t thread_count] [--framerate|-f framerate] [--label_path|-l label_path]  

Usage: ./camera_demo [--model_path|-m model_path] [--camera_index|-i index] [--width|-w width] [--height|-h height][--threads|-t thread_count] [--fps|-f framerate] [--label_path|-l label_path]

Usage: ./imagefile_demo [--model_path|-m model_path] [--input_filename|-i input_filename] [--label_path|-l label_path]

you can run the above command in your rk3588