Lichee Pi 4A

YOLOV5n

应用与综合 系统：revyos 更新：2026-05-08

RuyiSDK AI 示例

环境配置

开发板配置

安装 python 虚拟环境

sudo -i
apt install python3.11-venv
cd /root
python3 -m venv ort
source /root/ort/bin/activate
#下载 python 依赖包
cd /home/debian
git clone -b python3.11 https://github.com/zhangwm-pt/prebuilt\_whl.git
cd prebuilt\_whl
pip install \*.whl

SHL库安装

# 1. 安装 shl-python
pip3 install shl-python -i https://pypi.tuna.tsinghua.edu.cn/simple
 
# 2. 查看安装位置并复制动态库到系统目录
python3 -m shl --whereis th1520
# 假设输出为 /home/debian/ort/lib/python3.11/site-packages/shl/install_nn2/th1520
 
# 3. 根据输出路径复制动态库（注意替换实际路径）
sudo cp /home/debian/ort/lib/python3.11/site-packages/shl/install_nn2/th1520/lib/* /usr/lib/
sudo ldconfig

HHB-onnxruntime 安装
HHB-onnxuruntime 是移植了 SHL 后端（execution providers），让 onnxruntime 能复用到 SHL 中针对玄铁 CPU 的高性能优化代码。
CPU 版本

wget https://github.com/zhangwm-pt/onnxruntime/releases/download/riscv_whl_v2.6.0/hhb_onnxruntime_c920-2.6.0-cp311-cp311-linux_riscv64.whl
pip install hhb_onnxruntime_c920-2.6.0-cp311-cp311-linux_riscv64.whl

NPU版本

wget https://github.com/zhangwm-pt/onnxruntime/releases/download/riscv_whl_v2.6.0/hhb_onnxruntime_th1520-2.6.0-cp311-cp311-linux_riscv64.whl
pip install hhb_onnxruntime_th1520-2.6.0-cp311-cp311-linux_riscv64.whl

宿主机（x86）环境配置

安装 Docker

sudo apt update
sudo apt install -y ca-certificates curl
# 下载 Docker 官方安装脚本并执行
 
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh

安装成功后查看docker及其状态:

docker --version
sudo systemctl status docker

在终端显示如下：

licheepi@licheepi-virtual-machine:~$ docker --version
Docker version 29.4.1, build 055a478
licheepi@licheepi-virtual-machine:~$ sudo systemctl status docker
● docker.service - Docker Application Container Engine
     Loaded: loaded (/lib/systemd/system/docker.service; enabled;>
     Active: active (running) since Fri 2026-04-24 13:35:46 CST;

将当前用户加入 docker 组（免 sudo 运行）

sudo usermod -aG docker $USER
newgrp docker

拉取 HHB Docker 镜像

docker pull hhb4tools/hhb:latest

创建容器，名字为hhb_env

docker run -itd --name hhb_env hhb4tools/hhb:latest
#进入容器
docker exec -it hhb_env /bin/bash

下载Yolov5n模型
下载到示例目录 /home/example/th1520_npu/yolov5n 下：

mkdir -p /home/example/th1520_npu/yolov5n
cd /home/example/th1520_npu/yolov5n
#使用 wget 命令，从官方示例仓库中下载运行推理所必需的文件。  
wget https://raw.githubusercontent.com/zhangwm-pt/lpi4a-example/main/detection/yolov5/coco.names
wget https://raw.githubusercontent.com/zhangwm-pt/lpi4a-example/main/detection/yolov5/inference.py
wget https://raw.githubusercontent.com/zhangwm-pt/lpi4a-example/main/detection/yolov5/kite.jpg
wget https://raw.githubusercontent.com/zhangwm-pt/lpi4a-example/main/detection/yolov5/yolov5n.c
#克隆官方仓库并安装依赖  
git clone https://gitclone.com/github.com/ultralytics/yolov5.git
cd yolov5
pip3 install ultralytics

如果遇到python版本不兼容的问题：
在 Docker 容器内创建新的 Python 3.9 环境

# 安装 python3.9 和 venv
apt update
apt install -y python3.9 python3.9-venv
 
# 创建虚拟环境
python3.9 -m venv /opt/yolo-venv
 
# 激活虚拟环境
source /opt/yolo-venv/bin/activate

在新环境里安装依赖

# 升级 pip
pip install --upgrade pip
# 安装 ultralytics
pip install ultralytics
pip install opencv-python-headless -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install pandas
pip install tqdm
pip install seaborn
#下载权重文件
wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt
cd /home/example/th1520_npu/yolov5n/yolov5
# 导出模型
python3 export.py --weights yolov5n.pt --include onnx --imgsz 384 640

退出虚拟环境：

deactivate

HHB编译模型：
将 ONNX 模型交叉编译成 CPU 上可执行的程序，需要使用 hhb 命令。
编译时需要先进入到示例所在目录 /home/example/th1520_npu/yolov5n

cd /home/example/th1520_npu/yolov5n
cp yolov5/yolov5n.onnx .
hhb -D \
  --model-file yolov5n.onnx \
  --data-scale-div 255 \
  --board c920 \
  --input-name "images" \
  --output-name "/model.24/m.0/Conv_output_0;/model.24/m.1/Conv_output_0;/model.24/m.2/Conv_output_0" \
  --input-shape "1 3 384 640" \
  --quantization-scheme float16

在终端显示如下：

root@78776422f7c9:/home/example/th1520_npu/yolov5n# hhb -D \
>   --model-file yolov5n.onnx \
>   --data-scale-div 255 \
>   --board c920 \
>   --input-name "images" \
>   --output-name "/model.24/m.0/Conv_output_0;/model.24/m.1/Conv_output_0;/model.24/m.2/Conv_output_0" \
>   --input-shape "1 3 384 640" \
>   --quantization-scheme float16
[2026-05-11 06:26:10] (HHB LOG): Start import model.
[2026-05-11 06:26:11] (HHB LOG): Model import completed! 
[2026-05-11 06:26:11] (HHB LOG): Start quantization.
[2026-05-11 06:26:12] (HHB LOG): Start optimization.
[2026-05-11 06:26:12] (HHB LOG): Optimization completed!
[2026-05-11 06:26:12] (HHB LOG): Start conversion to csinn.
[2026-05-11 06:26:12] (HHB LOG): Conversion completed!
[2026-05-11 06:26:12] (HHB LOG): Start operator fusion.
[2026-05-11 06:26:13] (HHB LOG): Operator fusion completed!
[2026-05-11 06:26:13] (HHB LOG): Start operator split.
[2026-05-11 06:26:13] (HHB LOG): Operator split completed!
[2026-05-11 06:26:13] (HHB LOG): Start layout convert.
[2026-05-11 06:26:13] (HHB LOG): Layout convert completed!
[2026-05-11 06:26:13] (HHB LOG): Quantization completed!

退出docker环境。

exit

在终端显示如下：

root@78776422f7c9:/home/example/th1520_npu/yolov5n# exit
exit
licheepi@licheepi-virtual-machine:~$

安装 RuyiSDK

# 下载并安装 ruyi
wget https://mirror.iscas.ac.cn/ruyisdk/ruyi/tags/0.47.0/ruyi-0.47.0.amd64
chmod +x ruyi-0.47.0.amd64
sudo cp ruyi-0.47.0.amd64 /usr/local/bin/ruyi

安装工具链

ruyi update
ruyi install gnu-plct-xthead

会在终端看到如下输出：

info: skipping already installed package gnu-plct-xthead-3.1.0-ruyi.20250526

YOLOv5n

示例描述和硬件环境准备

示例描述：YOLOv5n 是轻量级目标检测模型，本示例在 Lichee Pi 4A 上运行 YOLOv5n，验证 RuyiSDK 工具链在 RISC-V CPU（C920）上的交叉编译能力。 硬件环境：Lichee Pi 4A (16GB)
软件环境：RuyiSDK 0.47.0，HHB 2.6.17

创建并激活 ruyi 虚拟环境

创建虚拟环境 venv-sipeed

ruyi venv -t gnu-plct-xthead sipeed-lpi4a venv-sipeed

激活虚拟环境

. venv-sipeed/bin/ruyi-activate

终端显示如下：

licheepi@licheepi-virtual-machine:~$ ruyi venv -t gnu-plct-xthead sipeed-lpi4a venv-sipeed 
info: Creating a Ruyi virtual environment at venv-sipeed...
info: The virtual environment is now created.
 
You may activate it by sourcing the appropriate activation script in the
bin directory, and deactivate by invoking `ruyi-deactivate`.
 
A fresh sysroot/prefix is also provisioned in the virtual environment.
It is available at the following path:
 
    /home/licheepi/venv-sipeed/sysroot
 
The virtual environment also comes with ready-made CMake toolchain file
and Meson cross file. Check the virtual environment root for those;
comments in the files contain usage instructions.
 
licheepi@licheepi-virtual-machine:~$ . venv-sipeed/bin/ruyi-activate 
«Ruyi venv-sipeed» licheepi@licheepi-virtual-machine:~$

使用 ruyi 工具链编译示例代码

确认交叉编译器可用

riscv64-plctxthead-linux-gnu-g++ --version
# 复制整个yolov5n 目录
docker cp hhb_env:/home/example/th1520_npu/yolov5n .

把 Docker 里的官方库复制到宿主机

docker cp hhb_env:/usr/local/lib/python3.8/dist-packages/hhb/install_nn2/th1520 ./hhb_th1520
 
docker cp hhb_env:/usr/local/lib/python3.8/dist-packages/hhb/prebuilt/runtime/riscv_linux ./hhb_runtime
 
docker cp hhb_env:/usr/local/lib/python3.8/dist-packages/hhb/prebuilt/decode/install/lib/rv ./hhb_decode_lib

修改yolov5n.c源码：

cd ~/yolov5n
sed -i 's/shl_ref_f32_to_input_dtype/shl_c920_f32_to_input_dtype/g' yolov5n.c

最终编译命令：

riscv64-plctxthead-linux-gnu-gcc \
  yolov5n.c \
  -o yolov5n_example \
  hhb_out/io.c \
  hhb_out/model.c \
  -Wl,--gc-sections \
  -O2 -g \
  -mabi=lp64d \
  -I . \
  -I hhb_out/ \
  -I ~/hhb_c920/include/ \
  -I ~/hhb_c920/include/shl_public/ \
  -I ~/hhb_c920/include/csinn/ \
  -L ~/hhb_c920/lib/ \
  -lshl \
  -L ~/hhb_decode_lib/ \
  -L ~/hhb_runtime/ \
  -lprebuilt_runtime \
  -ljpeg -lpng -lz -lstdc++ -lm \
  -march=rv64gcv0p7_zfh_xtheadc \
  -Wl,-unresolved-symbols=ignore-in-shared-libs

生成的可执行文件 yolov5n_example 用于在开发板上运行模型推理
解决 omp.h 缺失问题

mkdir -p /home/licheepi/venv-sipeed/sysroot.riscv64-plctxthead-linux-gnu/usr/include
docker cp hhb_env:/usr/lib/gcc/x86_64-linux-gnu/9/include/omp.h \
    /home/licheepi/venv-sipeed/sysroot.riscv64-plctxthead-linux-gnu/usr/include/
cp ~/omp.h ~/yolov5n/

运行示例并验证结果

把生成文件传递到开发板上：

scp -r /home/licheepi/yolov5n debian@172.16.60.209:~/

激活开发板上的python虚拟环境

source /home/debian/ort/bin/activate

运行程序

cd yolov5n
python3 inference.py

在终端显示如下：

(ort) debian@revyos-lpi4a:~/yolov5n$ python3 inference.py
 ********** preprocess image **********
 ******* run yolov5 and postprocess *******
Run graph execution time: 389.52063ms, FPS=2.57
detect num: 4
id:     label   score           x1              y1              x2              y2
[0]:    0       0.901887        274.524475      158.559036      359.169312      332.431702
[1]:    0       0.879545        80.073883       184.767792      190.130157      349.906281
[2]:    0       0.845318        219.378418      221.669983      283.860413      333.806152
[3]:    33      0.666908        67.099136       174.128189      202.971451      220.213608
 ********** draw bbox **********
[274.524475, 158.559036, 359.169312, 332.431702, 0.901887, 0]
[80.073883, 184.767792, 190.130157, 349.906281, 0.879545, 0]
[219.378418, 221.669983, 283.860413, 333.806152, 0.845318, 0]
[67.099136, 174.128189, 202.971451, 220.213608, 0.666908, 33]