Features

A feature within the context of Labelbox is an individual entry in an ontology. It contains all of the information required for rendering a specific annotation.

In other words, a feature is the master definition of:

How you want your model to do a given task (i.e., create predictions).
How your labeling team should label an asset (i.e., create ground truth).

You can think of a collection of features as a blueprint for providing structure to your data in the form of annotations. In the editor, you will see the list of features (AKA the ontology) in the Tools panel.

There are two kinds of features: objects (e.g., bounding box, polygon, etc) and classifications (e.g., radio, checklist, etc). A feature can have multiple deeply nested sub-classifications.

An annotation is simply a single instance of a feature.

Key definitions

Component	Definition
Object	Objects are features that generally are visible on the stage (canvas). This includes bounding boxes, segmentation masks, named entities, and relationships.
Classification	Classification is essentially a tag that can exist in any context (global or inside an object). Labelers assign classifications by completing a form in the Editor.
Nested classification	Classifications can be global (i.e., it applies to the entire asset) or they can be nested within an object-type annotation (i.e., a child of an object-type annotation).
Relationship	A relationship is a specialized annotation type that connects two annotations and allows you to name that connection.

Objects vs classifications

Labelbox separates its annotations into two general categories: objects and classifications. An easy way to remember the difference between the two is that object-type annotations are visible on the editor stage and classifications are not.

Structurally, objects cannot be nested within other objects. However, classifications can be nested within objects. Classifications can also be at the global level (can apply to the entire asset).

In this sample ontology, there is one object feature (named "Tree") and 3 classification features.

The classification question, "Is the image blurry?", is at the global level. This means this classification applies to the entire data row.

The classification questions, "Does the tree have leaves?" and "Is the tree alive?", are sub-classifications that apply only within the scope of the "Tree" object.