This repository contains a simple example to illustrate how to train the model with pytorch and evaluate the comprehensive performances in terms of detection performance, flash occupation and latency. You can try it by running the following commands on the given training dataset.
For this example, we implemented a convolutional neural network (CNN). You can use a different classifier and software for your implementation.
This code uses four main scripts, described below, to train and test your model for the given dataset.
You can run this classifier code by installing the requirements
pip install requirements.txt
and running
python training_save_deep_models.py
python testing_performances.py
where models is a folder of model structure file, saved_models is a folder for saving your models, data_indices is a folder of data indices (the given training dataset has been partitioned into training and testing dataset, you can create more partitions of the training data locally for debugging and cross-validation), and records is a folder for saving the statistics outputs. The TinyML Contest 2023 web-page provides a description of the data files.
After running the scripts, one of the scoring metrics (i.e., F-B) will be reported in the file seg_stat.txt in the folder records.
In this example, we will deploy the model on the board NUCLEO-L432KC with STM32CubeMX and the package X-Cube-AI.
You can firstly convert the model to onnx format by running
python pkl2onnx.py
Once we obtain the onnx model file, we could deploy the model on the board by following the instructions described in README-Cube.md. The other two metrics, Flash occupation and Latency could be obtained based on the reports from STM32CubeMX.
After training your model and obtaining test outputs with above commands, you could evaluate the scores of your models using the scoring function specified in TinyML Contest 2023 evaluation.