YOLO11

This version of YOLO11 has been converted to run on the Axera NPU using w8a16 quantization.

This model has been optimized with the following LoRA:

Compatible with Pulsar2 version: 3.4

Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through

• The repo of ax-samples, which you can get the how to build the ax_yolo11

• The repo of axcl-samples, which you can get the how to build the axcl_yolo11

• Pulsar2 Link, How to Convert ONNX to axmodel

Support Platform

• AX650
  • M4N-Dock(爱芯派Pro)
  • M.2 Accelerator card
• AX630C
  • 爱芯派2
  • Module-LLM
  • LLM630 Compute Kit

Chips	cost
AX650	25 ms
AX630C	TBD ms

How to use

Download all files from this repository to the device

(axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11 $ tree -L 2
.
├── ax620e
│   └── yolo11s.axmodel.onnx
├── ax650
│   ├── yolo11s.axmodel
│   └── yolo11x.axmodel
├── ax_aarch64
│   └── ax_yolo11
├── axcl_aarch64
│   └── axcl_yolo11
├── axcl_x86_64
│   └── axcl_yolo11
├── config.json
├── cut-onnx.py
├── football.jpg
├── README.md
├── ssd_horse.jpg
├── yolo11_config.json
├── yolo11_out.jpg
├── yolo11s-cut.onnx
└── yolo11-test.py

6 directories, 15 files

Inference

Input image:

Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)

root@ax650:~/samples/AXERA-TECH/YOLO11# ./ax_aarch64/ax_yolo11 -m ax650/yolo11x.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolo11x.axmodel
image file : football.jpg
img_h, img_w : 640 640
--------------------------------------
Engine creating handle is done.
Engine creating context is done.
Engine get io info is done.
Engine alloc io is done.
Engine push input is done.
--------------------------------------
post process cost time:4.20 ms
--------------------------------------
Repeat 1 times, avg time 24.56 ms, max_time 24.56 ms, min_time 24.56 ms
--------------------------------------
detection num: 9
 0:  94%, [ 757,  220, 1127, 1154], person
 0:  94%, [   0,  357,  314, 1112], person
 0:  93%, [1353,  339, 1629, 1037], person
 0:  91%, [ 494,  476,  659, 1001], person
32:  86%, [1231,  877, 1281,  922], sports ball
32:  73%, [ 774,  887,  828,  938], sports ball
32:  66%, [1012,  882, 1051,  927], sports ball
 0:  54%, [   0,  543,   83, 1000], person
 0:  46%, [1837,  696, 1877,  814], person
--------------------------------------

Output image:

Inference with M.2 Accelerator card

(axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11 $ ./axcl_aarch64/axcl_yolo11 -m ax650/yolo11x.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolo11x.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:1.38 ms
--------------------------------------
Repeat 1 times, avg time 24.73 ms, max_time 24.73 ms, min_time 24.73 ms
--------------------------------------
detection num: 9
 0:  94%, [ 757,  220, 1127, 1154], person
 0:  94%, [   0,  357,  314, 1112], person
 0:  93%, [1353,  339, 1629, 1037], person
 0:  91%, [ 494,  476,  659, 1001], person
32:  86%, [1231,  877, 1281,  922], sports ball
32:  73%, [ 774,  887,  828,  938], sports ball
32:  66%, [1012,  882, 1051,  927], sports ball
 0:  54%, [   0,  543,   83, 1000], person
 0:  46%, [1837,  696, 1877,  814], person
--------------------------------------