ONNX to HuskyLens 2 Installation Package GUI Tool

Convert ONNX models into kmodel format and package them into HuskyLens 2 ZIP installation packages.

• 中文版本

Features

• Supports YOLOv8n object detection
• Supports Python 3.10 and above
• Runs on Windows 10, Linux
• Quantization using training dataset images
• Uses uint8 quantization

Installation

Clone the Repository

git clone https://github.com/DFRobot/onnx2kmodel

Install Dependencies

windows

Download and install .NET 7.0:

https://downloadcd.dfrobot.com.cn/HUSKYLENS/dotnet-sdk-7.0.410-win-x64.exe

Ensure Python 3.10 or higher is installed, then run:

pip install  -r requirements.txt
pip install  nncase_kpu-2.10.0-py2.py3-none-win_amd64.whl

Linux

安装 dotnet7.0

Install .NET 7.0 (tested on Ubuntu 22.04):

sudo apt update
sudo apt install -y dotnet-sdk-7.0

Install Python dependencies:

pip install  -r requirements.txt
pip install  nncase-kpu

Mac

Not yet tested.

Configuration Files

Application Configuration -- app_conf.toml

Click “Save Config” in the GUI to automatically update this file. It will be used as the default configuration next time you open the UI.

[comm]
mode = "MindPlus"    #use models/datasets exported from MindPlus, mode="User"  for user directory
icon_file = ""       #Required PNG icon with transparent background for packaging
app_name_EN = "Cell\\nRecognition"  # App name (English), "\n" for line break
app_name_zh_CN = "细胞识别"			  #App name (Simplified Chinese)
app_name_zh_TW = "細胞識別"			  #App name (Traditional Chinese)
title_name_EN = "Cell Recognition"  #Title shown in HuskyLens (English)
title_name_zh_CN = "细胞识别"         #Title shown in HuskyLens (Simplified Chinese)
title_name_zh_TW = "細胞識別"         #Title shown in HuskyLens (Traditional Chinese)
det_threshold = 0.6                 #Default detection threshold (0–1)

[mindplus_options]
dataset_zip = ""   # Dataset ZIP exported from MindPlus
model_zip = ""     # Model ZIP exported from MindPlus

[user_options]
user_dir = ""   # Directory for custom files in User mode

Model Configuration kmodel_conf.toml

For advanced users familiar with nncase. Beginners can use the default settings without modification.

[compile_options]
target = "k230"  # "cpu"
dump_ir = false
dump_asm = false
dump_dir = "./dump"
input_file = ""
preprocess = true
input_type = "uint8"  # "uint8", "float32"
input_shape = [1, 3, 320, 320]
input_range = [0, 1]
input_layout = "NCHW"  # "NHWC"
swapRB = false
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
letterbox_value = 0
output_layout = "NCHW"  # "NHWC"

[ptq_options]
calibrate_method = "NoClip"  # "Kld", "NoClip"
finetune_weights_method = "NoFineTuneWeights"
quant_type = "uint8"  # "float32", "int8", "int16"
w_quant_type = "uint8"  # "float32", "int8", "int16"
dump_quant_error = false
dump_quant_error_symmetric_for_signed = false
quant_scheme = ""
quant_scheme_strict_mode = false
export_quant_scheme = false
export_weight_range_by_channel = false

Running the GUI

python app.py

GUI Workflow

Create HuskyLens Installation Package (MindPlus Mode)

• Select MindPlus mode.
• Choose the model ZIP exported from MindPlus.
• Choose the dataset ZIP exported from MindPlus.
• Select an icon (PNG with transparent background).
• Enter multi-language app names (required).
• Enter multi-language title names (required).
• Set a reasonable default detection threshold.
• (Optional) Click Save Config to use as the default next time.
• Click Convert & Package — after a few minutes (depending on your hardware), a ZIP installation package will appear in the same directory as app.py. ⚠️ Do not rename this zip file.

Create HuskyLens Installation Package (User Mode)

For experienced users familiar with YOLO dataset structure.

Directory Layout Example

In the same directory as app.py, create the following structure:

.
└── user_dir
    ├── best.onnx
    ├── data.yaml
    └── images
        └── train
            ├── capture_f845db40.png
            ├── capture_fc0e6b54.png
            ├── capture_fc577b9b.png
            ├── capture_fe2a84a1.png
            └── ......

Installing the Application on HuskyLens 2

• Copy the generated ZIP package to:Huskylens\storage\installation_package on the HuskyLens MTP device.
• On HuskyLens, open Model Installation → Local Installation.The app will install automatically.
• Return to the main menu to find and launch your new app.

Known Issues

• The GUI becomes unresponsive during model conversion (conversion runs on the main thread).
• Multi-language labels are not yet supported.