Training

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:

• Training Object Detection for Sequential Data
• Training UNet self-supervised Optical Flow
• Training a Classification Model
• Using a Sequential DataLoader to Create a Training Loop
• Pytorch RNN API

Note

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.