Smartly selecting and preparing application specific training data

In this section, we will discuss how much training samples are needed according to the situational context and highlight some important aspects when preparing your annotations on the positive training samples.

Let's start by defining the principle of object categorization and its relation to training data, which can be seen in the following figure:

Smartly selecting and preparing application specific training data

An example of positive and negative training data for an object model

The idea is that the algorithm takes a set of positive object instances, which contain the different presentations of the object you want to detect (this means object instances ...

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