YOLOv8

此文档讲解如何在安装了瑞莎智核 AX-M1 的 host 设备上运行 YOLOv8 示例应用

提示

可执行程序的编译方法请参考 AXCL-Samples 编译示例

预编译模型量化方式：w8a16

创建虚拟环境

Host

python3 -m venv .venv && source .venv/bin/activate

下载示例应用仓库

Host

pip3 install -U "huggingface_hub"
hf download AXERA-TECH/YOLOv8 --local-dir ./YOLOv8
cd YOLOv8

示例使用

模型推理

Host

chmod +x ./axcl_aarch64/axcl_yolov8
./axcl_aarch64/axcl_yolov8 -m ax650/yolov8s.axmodel -i football.jpg

rock@rock-5b-plus:~/ssd/axera/YOLOv8$ ./axcl_aarch64/axcl_yolov8 -m ax650/yolov8s.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolov8s.axmodel
image file : football.jpg
img_h, img_w : 640 640
--------------------------------------
axclrtEngineCreateContextt is done.
axclrtEngineGetIOInfo is done.

grpid: 0

input size: 1
    name:   images
        1 x 640 x 640 x 3


output size: 3
    name: /model.22/Concat_output_0
        1 x 80 x 80 x 144

    name: /model.22/Concat_1_output_0
        1 x 40 x 40 x 144

    name: /model.22/Concat_2_output_0
        1 x 20 x 20 x 144

==================================================

Engine push input is done.
--------------------------------------
post process cost time:0.91 ms
--------------------------------------
Repeat 1 times, avg time 3.79 ms, max_time 3.79 ms, min_time 3.79 ms
--------------------------------------
detection num: 7
 0:  93%, [ 757,  215, 1131, 1156], person
 0:  93%, [   0,  354,  311, 1104], person
 0:  93%, [1351,  342, 1627, 1032], person
 0:  91%, [ 488,  478,  661,  998], person
32:  87%, [ 773,  889,  829,  939], sports ball
32:  77%, [1231,  876, 1280,  922], sports ball
 0:  60%, [1840,  690, 1906,  809], person
--------------------------------------

yolov8 demo output