How to export data, with examples for each type of export along with details on optional parameters and filters.
To export data via the SDK, use export()
, a scalable and efficient method that allows streaming of unlimited data rows while providing a unique task object for tracking progress.
The previously available export_v2()
method has been deprecated and will be removed in version 7.0 of the SDK. If you’re currently using export_v2()
, we strongly encourage you to switch to export()
for its enhanced streamable implementation.
To learn how to export data rows from a project via the app UI, visit Export labels from project.
Data type | Annotation export formats | Project export | Model run export |
---|---|---|---|
Image | See export formats | See sample | See sample |
Video | See export formats | See sample | See sample |
Text | See export formats | See sample | See sample |
Geospatial | See export formats | See sample | See sample |
Documents | See export formats | See sample | Not supported yet |
Audio | See export formats | See sample | Not supported yet |
Conversational text | See export formats | See sample | Not supported yet |
HTML | See export formats | See sample | Not supported yet |
There are three ways to export data from Labelbox: export from Catalog, export from a labeling project, and export from a model run.
Below is the complete list of required and optional fields supported for exports.
Field | Description | Project export | Model run export | Catalog export |
---|---|---|---|---|
data_row | Contains the basic information of the data row: - id - row_data - global_key - data_row_details (optional, see below) | Always | Always | Always |
data_row_details | Contains additional details of the data row: - dataset_id - dataset_name - created_at - updated_at - last_activity_at - created_by | Optional | Optional | Optional |
media_attributes | See Media attributes | Optional | Optional | Optional |
attachments | See Attachments | Optional | Optional | Optional |
metadata_fields | See Metadata | Optional | Optional | Optional |
embeddings | Contains a list of dictionaries with precomputed and custom embeddings | Optional | Optional | Optional |
projects | Contains the ID of the project in which the data row was labeled. | Always | n/a | Optional |
<project_id> | Contains the following sections, which are expanded on below: - labels - project_details | Always | n/a | Optional |
labels | Contains a list of labels attached to this data row: - label_kind - version - id - annotations | Always | Always | Optional |
label_details | Contains details of each specific label: - created_at - updated_at - created_by - reviews | Optional | n/a | Optional |
performance_details | Contains label-specific performance details: - seconds_to_create - seconds_to_review - skipped - performance_details_v2 , which contains: - seconds_to_create - seconds_to_review - seconds_to_rework - seconds_total | Optional | n/a | Optional |
project_details | Contains project-specific information about this data row: - ontology_id - task_id - task_name - batch_id - batch_name - workflow_status - priority - selected_label_id - consensus_expected_label_count - workflow_history | Optional | n/a | Optional |
project_tags | See Project tags | Always | n/a | n/a |
experiments | Contains the ID of the model experiment(s) in which the data row was stored. | n/a | Always | Optional |
<model_experiment_id> | Contains the following sections, which are expanded on below: - name - runs | n/a | Always | Optional |
name | Name of the model. | n/a | Always | Optional |
runs | Contains the ID of the model run(s) in which the data row was stored. | n/a | Always | Optional |
<model_run_id> | Contains the following sections, which are expanded on below: - name - annotation_group_id - labels - predictions - split | n/a | Always | Optional |
name | Name of the model run. | n/a | Always | Optional |
run_data_row_id | Model run data row ID, similar to data_row_id but in a model run’s context. | n/a | Always | Optional |
labels | Contains a list of the ground truth labels attached to this data row and included in this model run: - label_kind - version - id - annotations | n/a | Always | Always |
predictions | Contains a list of predictions attached to this data row and included in this model run: - label_kind - version - id - annotations | n/a | Optional | Optional |
split | Contains the split the data row belongs to (either Training , Validation , or Test ). | n/a | Optional | Optional |
When you export data rows from a project, a model run, or Catalog, you can set parameters to include optional fields in the exports. The table below expresses the parameters available for each type of export.
Parameter | Project export | Model run export | Dataset export (Catalog) | Slice export (Catalog) |
---|---|---|---|---|
attachments | ✔ | ✔ | ✔ | ✔ |
metadata_fields | ✔ | ✔ | ✔ | ✔ |
embeddings | ✔ | ✔ | ✔ | ✔ |
data_row_details | ✔ | ✔ | ✔ | ✔ |
project_details | ✔ | - | ✔ | ✔ |
label_details | ✔ | - | ✔ | ✔ |
performance_details | ✔ | - | ✔ | ✔ |
interpolated_frames | ✔ | ✔ | ✔ | ✔ |
predictions | - | ✔ | - | - |
model_run_details | - | ✔ | - | - |
model_run_ids | - | - | ✔ | ✔ |
project_ids | - | - | ✔ | ✔ |
all_projects | - | - | ✔ | ✔ |
all_model_runs | - | - | ✔ | ✔ |
For explanations of each field and subfield, see Export glossary. For a detailed explanation of the project_ids
, model_run_ids
, all_projects
, and all_model_runs
parameters, see Export data rows from Catalog below.
To learn how to apply these filters, see the below sections specific to each export type.
You can use filters to select a subset of data rows to export. The table below contains the filters supported for each export type. You can apply multiple supported filters to the same export. Combinations of filters apply AND
operator logic.
Filter | Project export | Model run export | Dataset export (Catalog) | Slice export (Catalog) |
---|---|---|---|---|
last_activity_at | ✔ | - | ✔ | - |
label_created_at | ✔ | - | ✔ | - |
workflow_status | ✔ | - | - | - |
batch_ids | ✔ | - | - | - |
global_keys | ✔ | - | ✔ | - |
data_row_ids | ✔ | - | ✔ | - |
The last_activity_at
and label_created_at
filters take the structure of [<start_date>, <end_date>]
and can have the following formats:
YYYY-MM-DD
(this is an alias of YYYY-MM-DD 00:00:00
)YYYY-MM-DD hh:mm:ss
YYYY-MM-DDThh:mm:ss±hhmm
(ISO 8601)None
The ISO 8601 format allows you to specify the timezone, while the other two formats assume the timezone from the user’s workspace settings.
The last_activity_at
filter captures only the data rows where the following changes have been made in the specified time frame:
rowData
), external ID (externalId
), or global key (globalKey
)Data rows in multiple projects update last_activity_at
when such changes occur in any project containing the data rows.
The label_created_at
filter captures only the data rows where labels have been created in the specified time frame.
The workflow_status
filter allows you to export only the data rows in a specific status of a project’s workflow. The filter accepts the following values:
ToLabel
InReview
InRework
Done
This filter only accepts one value. For example, filters = {"workflow_status": "InReview"}
.
The batch_ids
filter allows you to export only the data rows in a specific batch or batches. This filter accepts a list of batch IDs. For example, filters = {"batch_ids": ["batch_id_1", "batch_id_2"]}
.
To get the batches sent to a project and their associated information, you can use the project.batches()
method. For more information, see Get the batches.
The global_keys
filter allows you to export only the data rows with the specified global keys within a project or dataset. This filter accepts a list containing up to 2,000 values. For example, filters = {"global_keys": ["global_key_1", "global_key_2"]}
.
The data_row_ids
filter allows you to export only the data rows with the specified IDs within a project or dataset. This filter accepts a list containing up to 2,000 values. For example, filters = {"data_row_ids": ["data_row_id_1", "data_row_id_2"]}
.
Streamable exports (compatible with SDK versions 3.56 and above) allow you to get real-time data flow and updates using any of the following streamable export methods:
data_row.export()
dataset.export()
model_run.export()
project.export()
slice.export()
The return type of these methods is an object of the class ExportTask
. This class serves as a wrapper around a Task Objects. Because of this relationship, most of the features present in the Task
class are also available in the ExportTask
class.
ExportTask
supports the following methods and properties from Task
:
An instance of an ExportTask
can be obtained via the export()
method on the classes mentioned above, or by executing the following:
To check if a task has a result/errors, the following methods can be executed:
To stream the results of exported data rows:
For fine-grained control over the streaming process, you can use a for loop to iterate through the converted items in the stream. This allows you to implement custom streaming logic, process partial results, or apply additional filtering.
You can define a particular offset to initiate streaming. In the given example, the stream will start from offset 25,548.
Selecting a random offset might result in positioning within the middle of a JSON string, and this behavior is entirely acceptable. The impact of this choice will become apparent in the output as soon as the streaming starts.
Likewise, a specific line can be specified. In the following example, the stream will skip the first 348 lines and start with the 349th line, where a single JSON string is considered a line.
offsets and lines are indexed starting from 0, thus with_line(3)
will start streaming from the 4th line.
The offset within with_offset()
cannot exceed the total size, and line in with_line()
cannot exceed the total number of lines returned by these methods; otherwise, a ValueError exception
will be raised.
ExportTask
has two methods to output the total size of the exported file and the total number of lines it contains:
You can store export results in a JSON file and log any errors for monitoring or further processing, like the following example:
You can cancel an ongoing export task before it completes, like the following example:
When you export data rows from a project, you can narrow down your data rows by label status, metadata, batch, annotations, and workflow history. Then, when you export from a project, you may choose to include or exclude certain attributes in your export.
See the table at the top of this page to find the JSON export formats for each data type.
You can export data rows and all their information from a Dataset or a Catalog Slice.
When exporting from Catalog, you can include information about a data row from all projects and model runs to which it belongs. Specifically, you can export the labels from multiple projects and/or the predictions from multiple model runs for the selected data rows. You can use the all_projects
and all_model_runs
parameters to get information from all projects and model runs attached to your data row.
As shown below, the project_ids
and model_run_ids
parameters accept a list of IDs.
See the table at the top of this page to find the JSON export formats for each data type.
See the table at the top of this page to find the JSON export formats for each data type.