Examples for all data types
The examples in this developer guide primarily use image assets. For sample approaches with other data modalities, please view the developer guide for importing data nested under each asset type in the Import/Export section of the table of contents.Create a dataset
The only required argument when creating a dataset is thename
. You can also specify the IAM integration that you have set up on your account if not specified or set to None
it will use your default integration. For more details. including how to get your IAM integrations; visit our dedicated IAM integration page
Get a dataset
Dataset methods
Create data rows
Special character handling
Please note that certain characters like#
,<
, >
and |
| are not supported in URLs and should be avoided in your file names to prevent loading issues.Please refer to https://datatracker.ietf.org/doc/html/rfc2396#section-2.4.3 on URI standards.A good test for the handling of special characters is to test URLs in your browser address bar — if the URL doesn’t load properly in your browser, it won’t load in Labelbox.row_data
. However, Labelbox strongly recommends supplying each data row with a global key upon creation.
Task results
The task result is a collection of details, typically information on the data rows associated with the task.Warning
Large results (over 150,000 data rows) can take up to 10 mins to process.Task errors
The task errors work similarly to the task results but contain information on any associated errors.Create a singular data row
Importing a local file
Upload a local file
You can upload a local file to Labelbox storage and use the returned URL to create a data row.Create data row from a local file
You can also create a data row from a local file directly without uploading it to storage. This creates the file URL and the data row in one step. However, this is not recommended since it increases the time it takes for your data row creation.Append to an existing dataset
You can add data rows to an existing dataset using the methods described above. First, get a dataset, then create the rows.Export data rows from a dataset
Filtering the result, you can obtain a list of asset URLs, global keys, or data row IDs.Export data rows with labels and predictions
For more details, see Export data rows from Catalog.Update a dataset
Delete a dataset
Deleting a dataset cannot be undoneThis method deletes the dataset along with all data rows in the dataset and any labels made on these data rows. This action cannot be reverted.
Dataset attributes
Get the basics
Get the data rows
Thedata_rows()
attribute is a relationship to the DataRow
objects in the dataset. The relationship retrieves a paginated collection of data rows. It is recommended to use exports to get direct data row details especially for larger data sets.
Get the number of data rows
Therow_count
is a cached attribute; thus, you must re-fetch the dataset after creating data rows to retrieve the updated value.