March 5, 2024

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App

• Smart select (beta) in Catalog offers three different ways to curate your data within a dataset: random, ordered, and cluster-based. To access this feature, go to Catalog, select a dataset that is larger than 100 data rows, and select “Smart select”.
• Cluster view (beta) in Catalog is a new interface that helps you discover and find similar data rows as well as labeling or model mistakes. See Cluster view for more details.
• Natural language search and similarity search in Catalog now support video and audio assets.
• You can now 1) import custom metrics per prediction and 2) filter/sort by prediction-level custom metrics. These added functionalities make it easier to use our model error analysis tools in the Model product. To learn more about prediction-level metrics on model runs, see Filters.
• You can now invoke Foundry models from within a project to generate pre-labels. In the project overview, you’ll see “suggested models” to help you select the best model for generating predictions. This feature is in beta.
• The new Audit trail feature provides a way for you to view all operations (create, update, and delete) on an annotation. This feature makes monitoring changes to annotations over time easier for auditing purposes.
• The new enhanced video player (beta) in Catalog offers new capabilities to help you curate, evaluate, and visualize video data. These new capabilities include basic play/pause controls, support for predictions, pre-labels, ground truth (bounding boxes, polylines, and points), and support for comparing model runs.
• If you export from an image or video labeling project that contains data rows labeled with segmentation masks, you will see a new property in the export file called composite_mask. Composite masks are a grouping of all segmentation masks on a label. To learn more, see Export image annotations.
• If you export from a model run that contains segmentation mask predictions, you will see a new property in the export file called composite_mask. To learn more, see Export image annotations.
• You can now have a workflow filter based on consensus average. For example, you can use this filter to send data rows with a consensus score over 80% straight to “Done”. To do this, create a new workflow step and select consensus average from the workflow filters.
• Boost express makes it easier for you to request help labeling data. Once you’ve set up a project, you can request up to fifteen labelers to work on your data. See our docs on Boost express to learn more.
• The billing tab in your Workspace settings now has a button that enables you to easily unsubscribe from Foundry.

• When you select a project in Annotate, you will see a new project overview UI that makes navigating your project easier.
• We have disabled the ability to download videos from the preview mode for all users.
• When you are in a project, you will now see a “Start” button with a dropdown menu containing options for labeling, reviewing, and any other tasks you have configured for your project.

• Google SSO users that have a profile picture on their account can now log in as expected.
• The issue causing large numbers of data rows to be stuck in the “To Label” task has been fixed.
• The Enterprise dashboard now displays the correct information for your organization.
• In the Data row details panel, the row data can no longer be displayed with the security token.

• Starting in mid-April, we will sunset export v1 on a rolling basis. To view the sunset date for your tier, see the Export v1 to v2 migration guide.

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Python SDK

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Version 3.65.0 (2024-03-05)

• Rerelease of 3.64.0

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Version 3.64.0 (2024-02-29)

• Client.get_catalog Add catalog schema class. Catalog exports can now be made without creating a slice first
• last_activity_at filter added to export_v2, allowing users to specify a datetime window without a slice

• Review related WebhookDataSource topics

• Added get_catalog notebook
• Update custom metrics notebook
• Update notebooks for video and image annotation import

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Version 3.63.0 (2024-02-19)

• Ability for users to install and use sdk with pydantic v.2. while still maintaining support for pydantic v1.
• ModelRun export() and export_v2() add model_run_details to support splits

• Add composite mask notebook

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Version 3.62.0 (2024-02-12)

• Support custom metrics for predictions (all applicable annotation classes)
• FoundryClient.run_app Add data_row identifier validation for running foundry app
• Client.get_error_status_code Default to 500 error if a server error is unparseable instead of throwing an exception

• DataRowMetadata, DataRowMetadataBatchResponse, _UpsertBatchDataRowMetadata Make data_row_id and global_key optional in all schema types

• ExportTask.__str__ Fix returned type in ExportTask instance representation

• Project.upsert_review_queue

• Update notebooks to new export methods
• Add model slice notebook
• Added support for annotation import with img bytes