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

# Axera-hed

This version of **Axera-hed** has been converted to run on the Axera NPU using **w8a16** quantization. It is mainly used for detecting whether motor vehicle drivers are wearing helmets in traffic scenarios.

## Supported Classes

This model is trained to detect the following **4 classes**:
1. **helmet**
2. **head**
3. **e-bike**
4. **bike**

Compatible with Pulsar2 version: 5.0.

## 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)
- **AX620E**
  - [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)

## 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:
![](test.jpg)![](test1.jpg)

run
```bash
python3 ax_hed_infer.py --model ./AX650/ax_ax650_hel_algo_V1.0.0.axmodel --img test.jpg
```

```bash
root@ax650:/pcd# python3 ax_hed_infer.py  --model ./AX650/ax_ax650_hel_algo_V1.0.0.axmodel --img test.jpg
[INFO] Available providers:  ['AxEngineExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.10.1s
[INFO] Model type: 2 (triple core)
[INFO] Compiler version: 6.0 79a1e641
Input_name: images, Output_name: ['output0', '167']
Preprocess time: 0.38 ms
Inference time: 17.19 ms
Total detect 2 objects
0: head  0.840 [75.0, 5.0, 110.0, 47.0]
1: bike  0.844 [46.0, 113.0, 143.0, 256.0]
```

Output image:
![](out.jpg)![](out1.jpg)