Covering everything you need to know in order to build AI products faster.
How to use the model Foundry for automated data labeling and enrichment
Learn how to harness the power of Model Foundry to automate and enrich data workflows in Labelbox.
How to build a powerful product recommendation system for retail
Learn how to leverage Labelbox's data-centric AI platform to build a robust AI-powered product recommendation system to power personalized experiences for retail.
A guide to the Data I/O process in Labelbox
In this guide, we take an in-depth look at the Data I/O process and offer a step-by-step guide to streamline your interaction with the Labelbox platform.
Using Meta's Segment Anything (SAM) model on video with Labelbox's model-assisted labeling
Learn how to use Meta’s Segment Anything (SAM) model with YOLOv8 to automatically detect, classify, and draw masks on video.
Using Meta’s Segment Anything (SAM) model with YOLOv8 to automatically classify masks
Learn how to use Meta’s Segment Anything (SAM) model with YOLOv8 to automatically classify masks.
How to accelerate image-text pair generation with BLIP-2
Learn how to use BLIP-2-generated captions to create pre-labels for images so that a specialized workforce can further improve the image captions.
Automatically label text with 96%+ accuracy using foundation models
Learn how to automatically label text with 96%+ accuracy by leveraging Labelbox's search capabilities, bulk classification, and foundation models.
How to create and label text layers from PDF documents for AI
Learn about the different types of PDF layers and how to import annotations to build robust AI models with contextual information from PDF documents.
Using Labelbox and Weights & Biases to fine tune your computer vision projects
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.
How to kickstart and scale your data labeling efforts
Learn how to effectively kickstart and scale your data labeling efforts to reduce cost, while maintaining the desired quality required for your use case.
