ML_Image_Classification

• TensorFlow image classification with TFLite and Vela-TFLite, converting C/C++ source files.

1. First step

1. Install virtual env

• If you haven't installed NuEdgeWise, please follow these steps to install Python virtual environment and choose NuEdgeWise_env.
• Skip if you have already done it.

2. Running

• The classfication.ipynb notebook will help you prepare data, train the model, and finally convert it to a TFLite and C++ file.

2. Work Flow

1. Data prepare

• Users can utilize classfication.ipynb to download easy datasets, prepare their custom datasets (or even download from other open-source platforms like Kaggle).
• classfication.ipynb will prepare the user's chosen dataset folder, supporting a general structure where the folder names correspond to class labels.

2. Training

• classfication.ipynb offers some attributes for training configuration.
• The strategy of this image classification training is transfer learning & fine-tunning
• The output is tflite model.

3. Test

• Use classfication.ipynb to test the tflite model.

4. Deployment

• Utilize classfication.ipynb to convert the TFLite model to Vela and generate C source/header files.
• Also support Label source/header files converting.
• The cmd.ipynb notebook will demonstrate how to use the script located in datasets\gen_rgb_cpp.py to convert an image to a bytes source file.

3. Inference code

• MCU:

  • The ML_SampleCode repositories are private. Please contact Nuvoton to request access to these sample codes. Link
    • ML_M460_SampleCode (private repo)
  • M55M1BSP (public)

• MPU:

  • MA35D1