Covering everything you need to know in order to build AI products faster.
Explore how to perform model error analysis using Labelbox, with a specific focus on utilizing the Gallery View and Model Metrics.
•June 15, 2023
Learn how to use Meta’s Segment Anything (SAM) model with YOLOv8 to automatically detect, classify, and draw masks on video.
•June 1, 2023
Learn how to iterate and rapidly fine-tune OpenAI large language models with Labelbox Model.
•March 22, 2023
Learn how you can use model metrics to surface low-performing classes, find and fix labeling errors, and improve the overall performance of the model before it hits production on real-world data.
•March 3, 2023
Learn how you can use Labelbox and Weights & Biases together to build better computer vision models. Follow a step-by-step workflow of data curation, annotation, model diagnostics and hyperparameter tuning.
•March 1, 2023
Learn how to ship better models faster by leveraging Labelbox Model. In this guide, we'll walk you through a COCO object detection example to get you onboarded in Model with your first project, model, and model run.
•January 25, 2023
Discover how to get started with active learning by leveraging the 3 techniques that consistently help ML teams more quickly identify what data will most dramatically improve model performance.
•November 16, 2022
Learn how you can use Labelbox Model to visually compare your ground truths and predictions to identify and fix label errors.
•October 10, 2022
Learn how you can use Model to configure, track, and compare essential model training hyperparameters alongside training data and data splits. Easily track and reproduce model experiments to observe the differences and share best practices with your team.
A great way to boost model performance is to surface edge cases on which the model might be struggling. You can fix those model failures with targeted improvements to your training data so that the model is better trained on these edge cases.
•October 5, 2022
