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, which you can get the detail of guide
Pulsar2 Link, How to Convert ONNX to axmodel
The repo of AXera Platform,which you can learn how to compile the C++ demo

Support Platform

AX650
- M4N-Dock(爱芯派Pro)
- M.2 Accelerator card
AX630C
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
	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
	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
	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
├── 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
│   │   ├── 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
│   │   ├── 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
│       ├── 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_onnx_infer_DEIMv2.py
│   ├── QRCode_onnx_infer_Nanodet.py
│   ├── QRCode_onnx_infer_v5.py
│   ├── QRCode_onnx_infer_v8.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:

C++

./ax_xxx_qrcode_batch -m xxx_npu1.axmodel -i images/

Output:

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