LSM6DSOX MLC - Access to datasets, decision tree and workflow to extend existing ST models
Hello,
I am working with the Machine Learning Core (MLC) of the LSM6DSOX and I would like to better understand how to extend or reuse existing ST models.
In particular, I am using preconfigured models available from ST (e.g. Activity Recognition), which are provided as .ucf files.
My goal is to:
reuse an existing ST model as a baseline
add my own labeled data (custom activities)
generate a new combined model without starting completely from scratch
For this reason, I would like to ask:
Is it possible to access or obtain the original datasets used by ST to train the provided MLC models?
Is there a way to extract or inspect the underlying decision tree from a .ucf configuration?
Is there an official workflow to extend an existing model (e.g. retrain by adding new data) rather than creating a new model from zero?
I understand that .ucf files represent the final register configuration, but I am interested in working at a higher level (model/data level) if possible.
Any guidance or recommended approach would be greatly appreciated.
Thank you for your support.