Blog

Recapping CVPR 2023: Key takeaways from Labelbox

Discover the overarching key themes from the sessions, demos, and papers of CVPR 2023.

How to confidently compare, test, and evaluate models for machine learning

Learn how to always choose the best and most efficient model for your use case. With Model Foundry, you can test, compare, and evaluate models to confidently select the best performing model.

Accelerate image segmentation with a new AI-powered solution

Learn how to accelerate image segmentation with Model Foundry and Labelbox's latest upgrades to the image editor.

How to automate labeling for product categorization with LLMs

Learn how to use LLMs to instantly generate predictions on product categorization and summarization.

Labelbox Model Foundry: Automating data tasks with foundation models

With the introduction of Model Foundry, we will bring the power of foundation models in Labelbox to perform complex tasks such as data labeling and data enrichment.

How to write more effective prompts for natural language search

Explore best practices for crafting successful prompts specifically for natural language searches.

Using reinforcement learning from human feedback to fine-tune large language models

Learn how reinforcement learning from human feedback can be used to train models on tasks that are harder to specify.

Pre-label and enrich data with bulk classifications

Learn more about how to organize and automate data labeling with bulk classifications.

GPT-4 vs PaLM: Assessing the predictive and generative performance of LLM models

We put GPT-4 and PaLM — two of the largest and most powerful LLMs developed to date — to the test. Learn how GPT-4 and PaLM perform on zero-shot predictive accuracy and generative ability.

Auto-Segment 2.0 powered by Meta's Segment Anything Model

The upcoming Auto-Segment 2.0 tool will apply the power of Meta AI’s recently released Segment Anything Foundation (SAM) Model in a workflow that helps users rapidly generate high performance training data for their most complex computer vision use cases.