This file specifies a list of all labels to be captured in the dataset for a labeler 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 heirarchy.
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.
An asset representing a box of Rice Krispies cereal could b labeled as: food\cereal\kellogs\ricekrispies
“food” - type
“cereal” - subtype
“kellogs” - main descriptor
“ricekrispies” - sub descriptor
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.