Create a model

Developer guide: Create a model via the Python SDK

A model is a directory where you create, manage, and compare a set of model runs related to a machine learning task. A machine learning task is specified by the ontology of data that you use for integration with a model training service.

Labelbox is comprised of an integrated suite of products that share a common structure of data rows, labels, and features. For example, in the diagram below, the global schema definition of a feature is used in ontologies for labeling data as well as model training, testing, and evaluation in the Model product. Labelbox is structured so you can re-use feature schemas to create ontologies specific for data labeling or model training tasks.

Create a model

To create a new model:

1. Go to Model and click New model.
2. Configure the model by giving it a name and a thumbnail.
3. Select an ontology. The ontology determines which data rows can be used for model training. Once you select an ontology, all data rows containing the annotations within the selected ontology in your entire Catalog will be available for inclusion. You can further narrow the scope by utilizing projects or datasets filters.
4. Click Create model with (n) data rows to create the model.