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Definitions

This section acts as a glossary for definitions used in our products and documentation.

The nomenclature of any new platform can be overwhelming at first. We have tried to conform to industry naming wherever possible in a bid to make the user experience both intuitive and welcoming to new users and experienced practitioners alike.

note

In the interests of brevity, we will refer to both image and video data as 'frames' in the definitions below.

TermDescription
AnnotationA marking of where a feature applies in a frame. It is represented by an identifier like a bounding box, polygon or key point
Bounding boxA rectangle used to annotate a feature by drawing the bounds of the feature
ClassificationA mutually-exclusive category applied to a frame
DatasetA collection of videos and/or images
FeatureAn object in a frame, or a classification applied to a frame. These can be used to identify something in a frame (object: 'this thing is an apple') or to classify the frame itself (classification: 'this frame has apples')
Key pointA dot used to annotate a feature by specifying its location
LabelLabels note relevant features in a frame and apply to a dataset used in model training. They are an annotation asserting which features in the desired ontology are true.
Label EditorThe UI for annotating data and managing labels
Micro-modelA model specifically trained to label a dataset for training other models
ModelA program with a set of functions and parameters that allow it to recognise features in datasets. Different models have different strengths and weaknesses
Model trainingThe process of teaching a model an ontology. This is done by algorithmically changing model parameters until it can reliably recognise features that are labelled in a dataset
Model inferenceThe process of using a trained model to predict the presence of features in new data
ObjectSomething of interest in a frame. Defined by string together with an annotation. It can be used as part of an ontology to label entities of interest in a dataset used for model training
Object detectionThe ability of a model to reliably recognise when a frame contains an object of interest. An application of model inference
Object trackingThe ability of a model to reliably detect and track objects in a sequence of frames over time. An application of model inference
OntologyA defined set of features and their relationships. This is what a model will be trained to apply to frames. Also known as a 'taxonomy'
PolygonA polygonal shape used to annotate a feature by drawing the bounds of a feature
ProjectA self-contained, collaborative environment for managing all productivity tasks associated with labeling and modelling 1 or more datasets
QualityAn assessment of the accuracy of a set of labels
Semantic segmentationThe application of labels to each pixel in a frame in order to classify segments of the frame as part of the same entity
TaskAn action required as part of the labeling workflow
Task ManagerThe UI for creating and managing tasks