Labelbox•March 2, 2023
Selecting high-impact data is crucial for improving model performance and informing your downstream ML workflow. This month, we're introducing a way for you to easily group and investigate specific slices of data in Model as well as analyze specific data rows based on metadata value. We are also introducing a new way for you to export your data in the Data Rows tab, giving you more control over which data rows and export fields you choose to export.
To accurately improve data quality and debug your model, you need a way to organize and surface specific data rows within a model run (a training experiment that contains a versioned snapshot of annotations, predictions, and metadata). These specific data rows can represent instances of rare data, edge cases, or test scenarios that you would want to monitor and inspect further.
Slices are now available in Model, allowing you to filter for specific data rows and save search queries:
The auto-generated slices provide a valuable starting point for evaluating your model’s effectiveness and can help you quickly surface any data quality issues or model weaknesses.
You can explore the auto-generated slices or save your first slice in Model today.
Metadata is non-annotation information on an asset that you can customize and upload to Labelbox.
After you explore and filter your data, you can export it to streamline AI workflows supported by adjacent tools and training environments within your pipeline.
We are making major improvements to the way you can export annotations from a labeling project. With the current way of exporting data, you can select which time range you want to export your data within, but you are limited in what other information you can choose to extract and export.
With this new and improved way of exporting data from your projects, you have more control over what type of information you choose to export:
You can now export more detailed information from your data rows via the UI as well as SDK. Choose to include or exclude relevant attributes in your export:
From the Data Rows tab in our UI, you can select and export a sub-selection of the data rows of most interest based on your predefined or new parameters:
While we will continue to support the old way of exporting data, we encourage you to test out an improved export functionality that aligns with the import format standard.
Please refer to our documentation for more detailed instructions on how to export your data through the UI or through the Python SDK.