# NuML_Studio

**Repository Path**: OpenNuvoton/NuML_Studio

## Basic Information

- **Project Name**: NuML_Studio
- **Description**: NuML_Studio is a UI tool for generating TFLM ML model projects and collecting real-time data for Nuvoton ML MCUs.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-03-23
- **Last Updated**: 2026-03-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# NuML_Studio

This is a UI tool for generating TFLM ML model projects and collecting real-time data for Nuvoton ML MCUs. It can be executed on Windows without the need to install Python or any related libraries.

## How this works
- The GitHub repository mainly provides source code. For a directly executable version, please download the .7z file from [release](https://gitee.com/OpenNuvoton/NuML_Studio/releases) and extract it to run.
    - If you want to install from source, refer to the "Build yourself" steps below
- Double-click `NuML_Studio.exe` to launch the tool.
    - If you want to upload to Edge Impulse or download single test data to deployment, please update your project API key in `API_key.txt`.

- [Quick start guide](https://gitee.com/OpenNuvoton/NuML_Studio/tree/main/doc/QuickStart-EIProject.md) for using NuML_Studio and Edge Impulse Project.

- Other `*.exe` files are command-line programs. Please refer to the examples in `cmd.txt` or use the `-h` option for help.
    - Users can update the corresponding `*.int` file for each `*.exe` to perform testing, or execute the program without entering parameters.

## Support Functions
#### NuMaker-M55M1
- **Project**: The first step in using NuML Studio is to create a project. You can create a project for data collection, ML deployment firmware generation, or both.
- **Collecting data**: click the `- Data Collection` tab, and click `Download Firmware to EVB` to flash [firmware](https://github.com/OpenNuvoton/ML_M55M1_CMSIS_SDS/tree/master) into your board.
    - Supports the following options, and users can modify them as needed:
        - G-sensor (3-axis): [SDS_Recorder_Gsensor_UART_CMSIS Firmware](https://github.com/OpenNuvoton/ML_M55M1_CMSIS_SDS/tree/master/M55M1BSP-3.01.002/SampleCode/SDS/SDS_Recorder_gsensor_uart_CMSIS)
        - Audio (16Khz): [SDS_Recorder_Audio_UART_CMSIS Firmware](https://github.com/OpenNuvoton/ML_M55M1_CMSIS_SDS/tree/master/M55M1BSP-3.01.002/SampleCode/SDS/SDS_Recorder_audio_uart_CMSIS)
        - Image (supports UVC using the NuMaker-M55M1's image sensor or the PC's webcam): [HSUSBH_USBH_UVC Firmware](https://gitee.com/OpenNuvoton/M55M1BSP/tree/master/SampleCode/StdDriver/HSUSBH_USBH_UVC)
    - Convert the data to standard format in `- Output` tab:
        - Users can select `csv format` to convert sensor `*.sds` data into `*.csv`.
        - Users can select `audio_wav format` to convert audio `*.sds` data  into `*.wav`.
        - Images collected from `Raw Image Collection` under `Data Collection` are already saved as `*.jpg`
     
- **Upload data to EdgeImpulse**:
    - Please update your Edge Impulse project API key in `API_key.txt`.
    - In `- Output` tab, user can enable `Edge Impulse format & export` to upload your data.
    - In `- Upload` tab, an entire data directory can be uploaded at once.

- **Generate ML Model Project**: 
    - Click the `Deployment`, `- Nuvoton` tab, and select your TFLite int8 model. Keil and VSCode CMSIS projects are supported.
        - Model inference firmware code generation.
        - G-sensor model with ARM SDS and RTX5 firmware code generation.
        - Image classification firmware code generation.
        - Object detection firmware code generation.
    - Click the `Deployment`, `- Edge Impulse` tab, and select your downloaded Edge Impulse SDK folder (Please  select deployment as `Ethos-U55-256 library`). Keil and VSCode CMSIS projects are supported. (Please update your Edge Impulse project API key in `API_key.txt`)
        - Model inference firmware code generation.
        - Image classification firmware code generation.
        - KWS firmware code generation.  


## Tools we use
- **NuML_TFLM_Tool**: A tool for generating ML model projects based on the TFLM framework. It is based on and references the [NuML_TFLM_Tool](https://github.com/OpenNuvoton/NuML_Toolkit/tree/master/NuML_TFLM_Tool).
- **sds_utilities**: A tool for collecting data to a local PC or uploading it to Edge Impulse. Based on the [SDS-Framework](https://github.com/ARM-software/SDS-Framework) and Edge Impulse.
- **Edge Impulse API**: https://docs.edgeimpulse.com/tools/libraries/sdks/studio/python/edgeimpulse/data

## Build yourself
- References:
    - [Embedded Python Releases for Windows](https://www.python.org/downloads/windows/)

    - [PyStand](https://github.com/skywind3000/PyStand)
- Steps:

    - Download the embedded Python `3.10.6` and copy it to the `runtime` directory.
    
    - In a Python virtual environment (venv), use pip to install the dependencies listed in `requirements.txt`, then copy the `site-packages` directory to here.
    **Warning**: The version of the Python virtual environment must exactly match the embedded Python version.
    
    - Call the CLI module's entry point directly as a Python module using the embedded Python
        - Edit `python310._pth` (depends on your version) in `runtime` to add your `site-packages`. (The example is in `runtime/python310._pth`)