YOLOv11-Pose
This document explains how to run the YOLOv11-Pose sample application on a host device equipped with the Radxa AICore AX-M1.
tip
For instructions on building the executable, see AXCL-Samples build examples.
Precompiled model quantization format: w8a16.
Create a virtual environment
Host
python3 -m venv .venv && source .venv/bin/activate
Download the demo repository
Host
pip3 install -U "huggingface_hub"
hf download AXERA-TECH/YOLO11-Pose --local-dir ./YOLO11-Pose
cd YOLO11-Pose
Example usage
Model inference
Host
chmod +x axcl_aarch64/axcl_yolo11_pose
./axcl_aarch64/axcl_yolo11_pose -m ax650/yolo11x-pose.axmodel -i football.jpg
(.venv) rock@rock-5b-plus:~/ssd/axera/YOLO11-Pose$ ./axcl_aarch64/axcl_yolo11_pose -m ax650/yolo11x-pose.axmodel -i football.jpg
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model file : ax650/yolo11x-pose.axmodel
image file : football.jpg
img_h, img_w : 640 640
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axclrtEngineCreateContextt is done.
axclrtEngineGetIOInfo is done.
grpid: 0
input size: 1
name: images
1 x 640 x 640 x 3
output size: 6
name: /model.23/Concat_1_output_0
1 x 80 x 80 x 65
name: /model.23/Concat_2_output_0
1 x 40 x 40 x 65
name: /model.23/Concat_3_output_0
1 x 20 x 20 x 65
name: /model.23/cv4.0/cv4.0.2/Conv_output_0
1 x 80 x 80 x 51
name: /model.23/cv4.1/cv4.1.2/Conv_output_0
1 x 40 x 40 x 51
name: /model.23/cv4.2/cv4.2.2/Conv_output_0
1 x 20 x 20 x 51
==================================================
Engine push input is done.
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post process cost time:0.40 ms
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Repeat 1 times, avg time 25.16 ms, max_time 25.16 ms, min_time 25.16 ms
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detection num: 6
0: 94%, [1350, 337, 1632, 1036], person
0: 93%, [ 492, 477, 658, 1000], person
0: 92%, [ 756, 219, 1126, 1154], person
0: 91%, [ 0, 354, 314, 1108], person
0: 73%, [ 0, 530, 81, 1017], person
0: 54%, [ 142, 589, 239, 1013], person
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yolov11-pose demo output