# Labeling
Many labelers require mapping the objects in the view to the values recorded in the dataset. As an example, Semantic Segmentation needs to determine the color to draw each object in the segmentation image.

This mapping is accomplished for a GameObject by:
* Finding the nearest Labeling component attached to the object or its ancestors.
* Find the first label in the Labeling which is present anywhere in the Labeler's Label Config.
* The resolved Label Entry from the Label Config is used to produce the final output.

## Labeling component
The `Labeling` component associates a list of string-based labels with a GameObject and its descendants. A `Labeling` component on a descendant overrides its parent's labels.

## Label Config
Many labelers require require a `Label Config` asset. This asset specifies a list of all labels to be captured in the dataset along with extra information used by the various labelers.

## Best practices
Generally algorithm testing and training requires a single label on an asset for proper identification such as “chair”, “table”, or “door". To maximize asset reuse, however, it is useful to give each object multiple labels in a hierarchy.

For example, an asset representing a box of Rice Krispies cereal could be labeled as `food\cereal\kellogs\ricekrispies`

* “food” - type
* “cereal” - subtype
* “kellogs” - main descriptor
* “ricekrispies” - sub descriptor

If the goal of the algorithm is to identify all objects in a scene that is “food” that label is available and can be used. Conversely if the goal is to identify only Rice Krispies cereal within a scene that label is also available. Depending on the goal of the algorithm any mix of labels in the hierarchy can be used.