YOLOv8

环境配置

信息

参考 RKNN 安装 配置好相关环境。

参考 RKNN Model Zoo 下载示例文件。

模型下载

下载 onnx 模型文件。

X64 Linux PC

cd rknn_model_zoo/examples/yolov8/model/
bash download_model.sh

模型转换

选择目标平台。

rk3588

X64 Linux PC

export TARGET_PLATFORM=rk3588

rk356x

X64 Linux PC

export TARGET_PLATFORM=rk356x

rk3576

X64 Linux PC

export TARGET_PLATFORM=rk3576

将 onnx 模型转换为 rknn 模型。

X64 Linux PC

cd ../python/
python convert.py ../model/yolov8n.onnx ${TARGET_PLATFORM}

C API

编译示例

切换到 rknn_model_zoo 目录下执行 build-linux.sh 编译脚本。

X64 Linux PC

cd ../../..
bash build-linux.sh -t ${TARGET_PLATFORM} -a aarch64 -d yolov8

文件同步

然后将编译生成的 install 目录下的 demo 目录推送到板端。

X64 Linux PC

cd install/${TARGET_PLATFORM}_linux_aarch64/
scp -r rknn_yolov8_demo/ user@your_device_ip:target_directory

运行示例

导出运行时库到环境变量。

Device

cd rknn_yolov8_demo/
export LD_LIBRARY_PATH=./lib

运行示例。

Device

./rknn_yolov8_demo model/yolov8.rknn model/bus.jpg

$ ./rknn_yolov8_demo model/yolov8.rknn model/bus.jpg
load label ./model/coco_80_labels_list.txt
model input num: 1, output num: 9
input tensors:
  index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
output tensors:
  index=0, name=318, n_dims=4, dims=[1, 64, 80, 80], n_elems=409600, size=409600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-58, scale=0.117659
  index=1, name=onnx::ReduceSum_326, n_dims=4, dims=[1, 80, 80, 80], n_elems=512000, size=512000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003104
  index=2, name=331, n_dims=4, dims=[1, 1, 80, 80], n_elems=6400, size=6400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003173
  index=3, name=338, n_dims=4, dims=[1, 64, 40, 40], n_elems=102400, size=102400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-45, scale=0.093747
  index=4, name=onnx::ReduceSum_346, n_dims=4, dims=[1, 80, 40, 40], n_elems=128000, size=128000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003594
  index=5, name=350, n_dims=4, dims=[1, 1, 40, 40], n_elems=1600, size=1600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003627
  index=6, name=357, n_dims=4, dims=[1, 64, 20, 20], n_elems=25600, size=25600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-34, scale=0.083036
  index=7, name=onnx::ReduceSum_365, n_dims=4, dims=[1, 80, 20, 20], n_elems=32000, size=32000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003874
  index=8, name=369, n_dims=4, dims=[1, 1, 20, 20], n_elems=400, size=400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
model is NHWC input fmt
model input height=640, width=640, channel=3
origin size=640x640 crop size=640x640
input image: 640 x 640, subsampling: 4:2:0, colorspace: YCbCr, orientation: 1
scale=1.000000 dst_box=(0 0 639 639) allow_slight_change=1 _left_offset=0 _top_offset=0 padding_w=0 padding_h=0
rga_api version 1.10.1_[0]
rknn_run
person @ (211 241 282 506) 0.864
bus @ (96 136 549 449) 0.864
person @ (109 235 225 535) 0.860
person @ (477 226 560 522) 0.848
person @ (79 327 116 513) 0.306
write_image path: out.png width=640 height=640 channel=3 data=0x32a58800

效果展示

Python API

激活虚拟环境

Device

conda activate rknn

运行示例

将相关文件推送到板端执行下面的命令。

Device

python yolov8.py --model_path ./model/yolov8.rknn --target ${TARGET_PLATFORM} --img_folder ./model --img_save

$ python yolov8.py --model_path ./model/yolov8.rknn --target rk3588 --img_folder ./model --img_save
/home/radxa/miniforge3/envs/rknn/lib/python3.12/site-packages/rknn/api/rknn.py:51: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  self.rknn_base = RKNNBase(cur_path, verbose)
I rknn-toolkit2 version: 2.3.2
--> Init runtime environment
I target set by user is: rk3588
done
Model-./model/yolov8.rknn is rknn model, starting val
W inference: The 'data_format' is not set, and its default value is 'nhwc'!


IMG: bus.jpg
person @ (211 241 282 506) 0.864
person @ (109 235 225 535) 0.860
person @ (477 226 560 522) 0.848
person @ (79 327 116 513) 0.306
bus  @ (96 136 549 449) 0.864
Detection result save to ./result/bus.jpg

效果展示