This directory contains notebook examples to run STM32 model zoo. These notebooks should be downloaded, then uploaded Jupyter or Colab.
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stm32ai_model_zoo_colab.ipynb shows how to train an image classification model on a custom or public dataset using our scripts.
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stm32ai_devcloud.ipynb shows how to access to the STM32Cube.AI Developer Cloud through ST Python APIs (based on REST API) instead of using the web application https://stm32ai-cs.st.com.
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stm32ai_quantize_onnx_benchmark.ipynb shows how to quantize ONNX format models with fake or real data by using ONNX runtime and benchmark it by using the STM32Cube.AI Developer Cloud. Other notebooks to fulfill model zoo features are available:
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imageclassification_deploy.ipynb allows to:
- Select the STM32 board
- Choose the Image Classification model
- Build the associated C project
- Flash the binary file to the STM32 board