Pre-trained networks are good for starting developing your applications and testing. However, nothing gives a boost to your performance as performing your own training on your own application-specific data. In this section, we will see how to train a network using event-based data.
There are two main ways of training your network:
supervised training requires labeled data, also known as Ground Truth (GT)
un-supervised training does not need labels and can learn directly from patterns in the data.
Depending on the type of training you want to use, follow one of our tutorials:
Obtaining the best results when performing your own training requires a deep knowledge of both machine learning and your application domain. Often, the best results are obtained by a trial-and-error process: experiment with different combinations of parameters, collect more data, choose a different pre-processing or noise filtering, etc.
Explore some more tutorials about training: