Catalog is a data curation tool for organizing, searching, visualizing, and exploring labeled and unlabeled data (including any metadata). Teams developing and operating production AI systems need a data catalog to enable data selection for downstream data-centric workflows. This includes data labeling, model training, model evaluation, error analysis, and active learning.
Data can flow into and out of the Catalog in numerous ways. This diagram indicates how data can flow through the Catalog.
Catalog can ingest the following pieces of data. Catalog makes it easy to search, visualize, and explore the following data in one place.
|Data rows & datasets||Data Rows in Catalog are imported when a dataset is created or appended with Data Rows.|
|Custom metadata||Custom metadata fields are imported during data row creation or update events.|
|Media attributes||Media attributes are a special class of metadata automatically pre-computed by Labelbox at data row creation or update events.|
The media attributes include file type, dimensions, and pre-computed embeddings. Media attributes are essential for your optimal experience with Labelbox.
|Ground truth annotations||You can view Ground truth annotations in the Catalog after creating annotations in Labelbox or importing the annotations to Labelbox.|
|Model predictions||Model predictions are not yet supported in Catalog.|
Data can flow out of the Catalog in two ways.
Data rows can be part of two collections in the Catalog.
|Datasets||Every data row in the Catalog belongs to exactly one dataset. Every data row in Catalog is imported when a dataset is created or appended to an existing dataset.|
|Slices||A data row in the Catalog may be part of any number of slices. A data row is part of a slice if it matches its associated filters.|
Updated 18 days ago