---
license: agpl-3.0
language:
- en
pipeline_tag: object-detection
tags:
- Axera
- YOLOv5
- NPU
- Ultralytics
- Object Detection
---

# pet-axera

This version of **pet-axera** has been converted to run on the Axera NPU using **w8a16** quantization. It is trained with modified yolov5n to detect pet(dog & cat) in our life.

## Supported Classes

This model is trained to detect the dogs and cats in our life with one label:
1. **pet**

Compatible with Pulsar2 version: 5.2.

## 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), where you can get the detailed guide.
- [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html)

## Support Platform

https://docs.m5stack.com/zh_CN/ai_hardware/AI_Pyramid-Pro

- **AX650N/AX8850**
  - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
  - [AI Pyramid](https://docs.m5stack.com/zh_CN/ai_hardware/AI_Pyramid-Pro)
  - [M.2 Accelerator card](https://docs.m5stack.com/en/ai_hardware/LLM-8850_Card)

## How to use

Download all files from this repository to the device.

### python env requirement

#### pyaxengine

https://github.com/AXERA-TECH/pyaxengine

```bash
wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc2/axengine-0.1.3-py3-none-any.whl
pip install axengine-0.1.3-py3-none-any.whl
```

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

Input image:
![](pet.jpg)

run
```bash
python3 axmodel_infer_pet.py
```

```bash
root@ax650:~/pet-axera# python3 axmodel_infer_pet.py
[INFO] Available providers:  ['AxEngineExecutionProvider', 'AXCLRTExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 2 (triple core)
[INFO] Compiler version: 5.2 eccb31f5
class: pet left:342 top:184 right:598 bottom:680 conf: 92%
class: pet left:660 top:293 right:943 bottom:878 conf: 95%
Saved res to axmodel_res.jpg

```

Output image:
![](axmodel_res.jpg)
```