Data processing is a fundamental step in converting event-based data into a format that is compatible with machine learning frameworks.
In this section, we will see:
How to pre-process data to create tensors. Our training loops and our inference pipelines use tensors as inputs. This section will show you how to pre-process event-based data into a format that is compatible with PyTorch.
How to convert a dataset into HDF5 files compatible with the Metavision ML training loops. Training loops require a dataset in HDF5 format, which is both smaller and faster than DAT or RAW files for machine learning.
Tutorials in this section were created using Jupiter Notebooks. You can execute them on your computer by downloading the source code at the top or bottom of the page. More information can be found on this page.