QRCode-axera

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
  • 爱芯派2
  • Module-LLM
  • LLM630 Compute Kit
• AX637

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:

C++

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

Output: