May 7, 2024

Release notes



  • Our new warehouse integration provides an easy, no-code solution to keep your datasets in Labelbox in sync with the tables in your data warehouse. You can use this integration to connect over 25 different data sources, including Big Query, Databricks, Snowflake, and Google Sheets. To learn more, see our Census integration docs.
  • You can use the new fine-tuning capability (beta) to fine-tune a YoloV8 object detection model with custom features. To run fine-tuning, you'll need to create a model experiment, import data rows and ground truth, and add an ontology to use for fine-tuning. Then, select the "fine-tune model" button to configure the training job. Docs on this feature are coming soon.
  • You can now include custom and auto-generated embeddings when you export your data rows via Export v2. You can find an example of exported embeddings on this page: Export image annotations.
  • When you create an issue, you now have the option to assign the issue to a category. Issue categories can be viewed and managed in the Issues tab in a project. To learn more, see our docs on Issues.
  • The following model + annotation types are now supported in Foundry:
    • Video bounding box detection via GroundingDINO model
    • Video segmentation mask detection via GroundingDINO + SAM model
    • Video frame-based classification via Google Gemini 1.5 Pro (Beta) model
    • Video global classification via X-Clip, Gemini Pro Vision, Gemini 1.5 Pro (Beta) models
  • We introduced new ways to create model experiments in the UI: 1) From the Manage selection dropdown in Catalog, you can send selected data rows to a new experiment or an existing experiment. 2) From Model, you can now click Create -> Experiment to create a new experiment. 3) From an existing experiment, you can now click + New Model Run to append data rows to the experiment.
  • LlaMa v3 is now supported in Foundry.


  • Our updated home page provides you with key actions, featured reads, and entry points to make it easier to get started on projects.
  • For projects that are configured with Boost Workforce Express, labeler instructions are now added as part of the ontology. We also enabled hyperlinking in the ontology Instructions to allow you to link a Google doc as labeler instructions.
  • With the new Catalog search experience, you can quickly search your data in Catalog by typing into the data search filter bar. You no longer need to specify a filter to find the data you are looking for. See Filters for more details.
  • The rate limit for Gemini 1.5 Pro increased from 5 to 600 requests per minute for Labelbox Foundry.


  • On April 19, we sunsetted Export v1 for free, edu, and starter customers. To learn more about this deprecation, see our migration guide.


  • On May 15, we will begin sunsetting Export v1 for pro, standard, and enterprise customers. To learn more, see our migration guide.
  • On June 30, we will be sunsetting the custom editor for all customers.

Python SDK

The latest version of our Python SDK is v3.69.1. See our full changelog in Github for more details on what was added recently.

Version 3.69.1 (2024-05-01)


  • Fixed a bug with certain types of content not being returned as a result of ExportTask.result or ExportTask.errors

Version 3.69.0 (2024-04-25)


  • Support to export embeddings from the SDK


  • Used OpenCV's headless library in replacement of OpenCV's default library

Version 3.68.0 (2024-04-16)


  • Added support for embeddings.
  • Introduced the use of 'rye' as a package manager for SDK contributors.
  • Implemented a unified create method for AnnotationImport, MEAPredictionImport, and MALPredictionImport.
  • Enhanced annotation upload functionality to accept data row IDs, global keys, or external IDs directly for


  • Ensure items in dataset.upsert_data_rows are not empty
  • Streamable export fix to report export_v2 errors as list of dictionaries, compatible with older releases

Version 3.67.0 (2024-04-05)


  • Added file
  • Made export_v2 methods use streamable backend
  • Added support for custom embeddings to dataset create data row(s) methods
  • Added ability to upsert data rows via dataset.upsert_data_rows() method
  • Added AssetAttachment with an ability to update() and delete()


  • Added check for 5000 labels per annotation per data row


  • Errors and Failed data rows are included in the task.result for dataset.create_data_rows()
  • Fixed 500 error handling and reporting


  • Updated import notebook for image data
  • Added attachment PDF example, removed requirements around text_layer_url
  • Included the get_catalog() method to the export notebook
  • Added workflow status filter to export_data notebook for projects
  • Send predictions to a project demo
  • Removed model diagnostic notebooks