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YOLOv11

此文档讲解如何在安装了瑞莎智核 AX-M1 的 host 设备上运行 YOLOv11 示例应用, 代码和编译方法请参考 ax_yolo11_steps.cc 和 ax-sample

预编译模型量化方式：w8a16

下载示例应用仓库​

使用 huggingfcae-cli 下载示例应用仓库。

Host
pip3 install -U "huggingface_hub[cli]"
huggingface-cli download AXERA-TECH/YOLO11 --local-dir ./YOLO11
cd YOLO11

示例使用​

模型推理​

Host
chmod +x axcl_yolo11
./axcl_yolo11 -m ax650/yolo11s.axmodel -i football.jpg
(.venv) rock@rock-5b-plus:~/ssd/axera/YOLO11$ ./axcl_yolo11 -m ax650/yolo11s.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolo11s.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.23/Concat_output_0
        1 x 80 x 80 x 144

    name: /model.23/Concat_1_output_0
        1 x 40 x 40 x 144

    name: /model.23/Concat_2_output_0
        1 x 20 x 20 x 144

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

Engine push input is done.
--------------------------------------
post process cost time:0.89 ms
--------------------------------------
Repeat 1 times, avg time 3.41 ms, max_time 3.41 ms, min_time 3.41 ms
--------------------------------------
detection num: 7
 0:  95%, [ 759,  213, 1126, 1152], person
 0:  94%, [   0,  359,  315, 1107], person
 0:  94%, [1350,  344, 1629, 1036], person
 0:  89%, [ 490,  480,  658,  996], person
32:  73%, [ 771,  888,  830,  939], sports ball
32:  67%, [1231,  876, 1280,  924], sports ball
 0:  62%, [   0,  565,   86,  995], person
--------------------------------------

yolov8 demo output