---
license: mit
language:
- en
base_model:
- Ultralytics/YOLOv8
pipeline_tag: object-detection
tags:
- Ultralytics
- YOLOv8
---

# YOLOv8

This version of YOLOv8 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 AXera Platform](https://github.com/AXERA-TECH/ax-samples), which you can get the detial of guide

- [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) 


## Support Platform

- AX650
  - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
  - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
- AX630C
  - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html)
  - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM)
  - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit)
  
|Chips|yolov8s|
|--|--|
|AX650| 3.6 ms |
|AX630C| TBD ms |

## How to use

Download all files from this repository to the device

```

root@ax650:~/YOLO11-Pose# tree
.
|-- ax650
|   `-- yolov8s.axmodel
|-- ax_yolov8
|-- football.jpg
`-- yolov8_out.jpg
```

### Inference

Input image:
![](./football.jpg)

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

```
root@ax650:~/samples/AXERA-TECH/YOLOv8# ./ax_yolov8 -m ax650/yolov8s.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolov8s.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:3.96 ms
--------------------------------------
Repeat 1 times, avg time 3.64 ms, max_time 3.64 ms, min_time 3.64 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
--------------------------------------
```

Output image:
![](./yolov8_out.jpg)


#### Inference with M.2 Accelerator card

```
(base) axera@raspberrypi:~/lhj/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:0.98 ms 
--------------------------------------
Repeat 1 times, avg time 3.75 ms, max_time 3.75 ms, min_time 3.75 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
--------------------------------------
```

Output image:
![](./yolov8_axcl_out.jpg)