README.md

---
license: mit
pipeline_tag: object-detection
---

# QRCode_det

This version of QRCode detetion model 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: 5.1

## Convert tools links:

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

- [The original repo](https://github.com/wzf19947/QRCode_det), which you can get the detail of guide

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

- [The repo of AXera Platform](https://github.com/AXERA-TECH/ax-samples),which you can learn how to compile the C++ demo

## 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)
- AX637

|Chips|model|cost|
|--|--|--|
||yolov5n|1.73 ms|
||yolov8n|3.64 ms|
||yolov9t|4.75 ms|
|AX650|yolov10n|3.67 ms|
||yolo11n|3.42 ms|
||yolo12n|6.87 ms|
||yolo26n|3.24 ms|
||NanodetPlus|2.16 ms|
||DEIMv2_femto(u16)|3.76 ms|
|||
||yolov5n|5.79 ms|
||yolov8n|9.26 ms|
||yolov9t|11.6 ms|
|AX630C|yolov10n|9.71 ms|
||yolo11n|9.65 ms|
||yolo12n|20.24 ms|
||yolo26n|10.04 ms|
||NanodetPlus|5.93 ms|
|||
||yolov5n|2.11 ms|
||yolov8n|4.04 ms|
||yolov9t|4.91 ms|
|AX637|yolov10n|4.05 ms|
||yolo11n|3.84 ms|
||yolo12n|6.40 ms|
||yolo26n|3.50 ms|
||NanodetPlus|2.38 ms|

## How to use

Download all files from this repository to the device

```

.
├── config.json
├── CPP
│   ├── ax_deimv2_qrcode_batch
│   ├── ax_nanodetplus_qrcode_batch
│   ├── ax_yolov5_qrcode_batch
│   ├── ax_yolov8_qrcode_batch
│   └── ax_yolo26_qrcode_batch
├── cpp_result.png
├── images
│   ├── qrcode_01.jpg
│   ├── qrcode_02.jpg
│   ├── qrcode_03.jpg
|   ├── ...
│   └── qrcode_55.jpg
├── model
│   ├── AX620E
│   │   ├── nanodet-plus-m_630_npu1.axmodel
│   │   ├── yolo11n_630_npu1.axmodel
│   │   ├── yolo12n_630_npu1.axmodel
│   │   ├── yolo26n_630_npu1.axmodel
│   │   ├── yolov10n_630_npu1.axmodel
│   │   ├── yolov5n_630_npu1.axmodel
│   │   ├── yolov8n_630_npu1.axmodel
│   │   └── yolov9t_630_npu1.axmodel
│   ├── AX637
│   │   ├── nanodet-plus-m_637_npu1.axmodel
│   │   ├── yolo11n_637_npu1.axmodel
│   │   ├── yolo12n_637_npu1.axmodel
│   │   ├── yolo26n_637_npu1.axmodel
│   │   ├── yolov10n_637_npu1.axmodel
│   │   ├── yolov5n_637_npu1.axmodel
│   │   ├── yolov8n_637_npu1.axmodel
│   │   └── yolov9t_637_npu1.axmodel
│   └── AX650
│       ├── deimv2_femto_650_npu1_u16.axmodel
│       ├── nanodet-plus-m_650_npu1.axmodel
│       ├── yolo11n_650_npu1.axmodel
│       ├── yolo12n_650_npu1.axmodel
│       ├── yolo26n_650_npu1.axmodel
│       ├── yolov10n_650_npu1.axmodel
│       ├── yolov5n_650_npu1.axmodel
│       ├── yolov8n_650_npu1.axmodel
│       └── yolov9t_650_npu1.axmodel
├── py_result.png
├── python
│   ├── QRCode_axmodel_infer_DEIMv2.py
│   ├── QRCode_axmodel_infer_Nanodet.py
│   ├── QRCode_axmodel_infer_v5.py
│   ├── QRCode_axmodel_infer_v8.py
│   ├── QRCode_axmodel_infer_26.py
│   ├── QRCode_onnx_infer_DEIMv2.py
│   ├── QRCode_onnx_infer_Nanodet.py
│   ├── QRCode_onnx_infer_v5.py
│   ├── QRCode_onnx_infer_v8.py
│   ├── QRCode_onnx_infer_26.py
│   └── requirements.txt
└── README.md


```

### Inference

Input Data:

```
|-- images
|   `-- qrcode_01.jpg
|   `-- qrcode_02.jpg
|   `-- qrcode_03.jpg
|   `-- qrcode_04.jpg...
```


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

##### Python
run with python3 QRCode_axmodel_infer_xxx.py
```
root@ax650:~/QRCode# python3 QRCode_axmodel_infer_DEIMv2.py
[INFO] Available providers:  ['AxEngineExecutionProvider']
[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: 4.2 b98901c3
识别成功！
图片 ./qrcode_test/qrcode_01.jpg 处理耗时: 0.2165 秒
识别成功！
图片 ./qrcode_test/qrcode_02.jpg 处理耗时: 0.1540 秒
识别成功！
图片 ./qrcode_test/qrcode_03.jpg 处理耗时: 0.1456 秒
识别成功！
图片 ./qrcode_test/qrcode_05.jpg 处理耗时: 0.1449 秒

```

Output:
![alt text](py_result.png)

##### C++
```
./ax_xxx_qrcode_batch -m xxx_npu1.axmodel -i images/
```

Output:
![alt text](cpp_result.png)