Blog

Prevent duplicate data uploads, save data slices, metadata metrics, and more

We've introduced a new system to identify data rows to allow teams to query faster and prevent duplicate uploads, introduced metadata analytics, and are making a host of exciting improvements to our embedding capabilities.

Better visualize and track model errors at every iteration

Find and fix label errors for text data, understand model weaknesses with in-depth metrics, and easily compare results for different model runs with different hyperparameters.

Accelerate AI development and search for more granular data with new features

We’ve focused recent product development on a host of improvements to help drive greater insight and productivity across annotation, data curation, and model training workflows.

Labelbox joins Air Force’s $950 million JADC2 effort

Labelbox today announced it is one of the companies who have been awarded a $950 million ceiling indefinite-delivery/indefinite-quantity contract by the U.S. Air Force.

Iterate and ship models faster with our newest features

We’ve focused recent product development on end-to-end workflow to help teams iterate and ship models faster than ever. Curate and prepare training datasets, have greater visibility into the processing status and searchability of your data rows, SDK updates, and more.

3 Things we learned at CVPR 2022

This year’s CVPR conference featured over two thousand papers, showing a few emerging themes and trends in the computer vision field.

Use automation to reduce your labeling time and spend

Labelbox is excited to introduce our image auto-segmentation tool to help teams quickly generate and edit quality segmentation masks in seconds.

How VirtuSense built an AI data engine that dramatically increased model performance

Virtusense is the #1 fall prevention system in the world. To train and maintain their models, the team designed their data engine to automate nearly every aspect of the iteration loop.

6 key best practices for AI teams to save time when creating AI data

Wading through a vast amount of unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. Learn six key time-saving practices for ML teams to implement when handling AI data.

Optimize the efficiency and quality of your training data with our newest features

We’ve focused recent product development to help users better optimize the throughput, efficiency, and quality of training data production. You can now more powerfully search for data rows within a project, visualize ground truth and predictions in Catalog, and more.