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---
license: mit
language:
- en
base_model:
- Ultralytics/YOLOv8
pipeline_tag: object-detection
tags:
- Ultralytics
- YOLOv8
- YOLOv8-Seg
---
# YOLOv8-Seg
This version of YOLOv8-Seg 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-seg|
|--|--|
|AX650| 4.6 ms |
|AX630C| TBD ms |
## How to use
Download all files from this repository to the device
```
root@ax650:~/YOLOv8-Seg# tree
.
|-- ax650
| `-- yolov8s-seg.axmodel
|-- ax_yolov8_seg
|-- football.jpg
`-- yolov8_seg_out.jpg
```
### Inference
Input image:
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
```
root@ax650:~/samples/AXERA-TECH/YOLOv8-Seg# ./ax_yolov8_seg -m ax650/yolov8s_seg.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolov8s_seg.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.
--------------------------------------
input size: 1
name: images [UINT8] [BGR]
1 x 640 x 640 x 3
output size: 7
name: /model.22/Concat_1_output_0 [FLOAT32]
1 x 80 x 80 x 144
name: /model.22/Concat_2_output_0 [FLOAT32]
1 x 40 x 40 x 144
name: /model.22/Concat_3_output_0 [FLOAT32]
1 x 20 x 20 x 144
name: /model.22/cv4.0/cv4.0.2/Conv_output_0 [FLOAT32]
1 x 80 x 80 x 32
name: /model.22/cv4.1/cv4.1.2/Conv_output_0 [FLOAT32]
1 x 40 x 40 x 32
name: /model.22/cv4.2/cv4.2.2/Conv_output_0 [FLOAT32]
1 x 20 x 20 x 32
name: output1 [FLOAT32]
1 x 32 x 160 x 160
post process cost time:16.21 ms
--------------------------------------
Repeat 1 times, avg time 4.69 ms, max_time 4.69 ms, min_time 4.69 ms
--------------------------------------
detection num: 8
0: 92%, [1354, 340, 1629, 1035], person
0: 91%, [ 5, 359, 314, 1108], person
0: 91%, [ 759, 220, 1121, 1153], person
0: 88%, [ 490, 476, 661, 999], person
32: 73%, [1233, 877, 1286, 923], sports ball
32: 63%, [ 772, 888, 828, 937], sports ball
32: 63%, [ 450, 882, 475, 902], sports ball
0: 55%, [1838, 690, 1907, 811], person
--------------------------------------
```
Output image:
#### Inference with M.2 Accelerator card
```
(base) axera@raspberrypi:~/lhj/YOLOv8-Seg $ ./axcl_aarch64/axcl_yolov8_seg -m ax650/yolov8s_seg.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolov8s_seg.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: 7
name: /model.22/Concat_1_output_0
1 x 80 x 80 x 144
name: /model.22/Concat_2_output_0
1 x 40 x 40 x 144
name: /model.22/Concat_3_output_0
1 x 20 x 20 x 144
name: /model.22/cv4.0/cv4.0.2/Conv_output_0
1 x 80 x 80 x 32
name: /model.22/cv4.1/cv4.1.2/Conv_output_0
1 x 40 x 40 x 32
name: /model.22/cv4.2/cv4.2.2/Conv_output_0
1 x 20 x 20 x 32
name: output1
1 x 32 x 160 x 160
==================================================
Engine push input is done.
--------------------------------------
post process cost time:3.67 ms
--------------------------------------
Repeat 1 times, avg time 4.85 ms, max_time 4.85 ms, min_time 4.85 ms
--------------------------------------
detection num: 8
0: 92%, [1354, 340, 1629, 1035], person
0: 91%, [ 5, 359, 314, 1108], person
0: 91%, [ 759, 220, 1121, 1153], person
0: 88%, [ 490, 476, 661, 999], person
32: 73%, [1233, 877, 1286, 923], sports ball
32: 63%, [ 772, 888, 828, 937], sports ball
32: 63%, [ 450, 882, 475, 902], sports ball
0: 55%, [1838, 690, 1907, 811], person
--------------------------------------
```
Output image:
|