The Master Guide to Data Curation and Auto-Curation for Computer Vision

The choices that are made when curating data are the most crucial to producing a high-performing and accurate computer vision model. In this detailed guide, you’ll receive a solid understanding of both traditional curation processes as well as how to optimize the data curation process through automation.

How to Automate Data Labeling using Transfer, Few-Shot, and Self-Supervised Learning

In this paper, we dive into our Custom Auto-Label workflow and discuss the algorithmic components of the product and the corresponding user experience. We introduce how Custom Auto-Label builds upon cutting-edge transfer, few-shot, and self-supervised learning techniques and presents a novel structure for semi-automated data labeling. These techniques provide all users with easy-to-use labeling automation features and allow clients to customize/fine-tune models in an automated fashion.

How to Improve Data Labeling Efficiency with Auto-Labeling, Uncertainty Estimates, and Active Learning

In this whitepaper, we dive into the machine learning theory and techniques that were developed to evaluate our auto-labeling AI. More specifically, how the platform estimates the uncertainty of auto-labeled annotations and applies it to active learning. This whitepaper will help you measure and evaluate how much you can trust the model output when utilizing auto labeling for data annotation.

Picking the Right Training Data Platform

This whitepaper guides you through the initial process of building an ML data pipeline by diving into the 4 pillars of an enterprise grade training data platform. How do experts evaluate the plethora of options available to them and pick an ML data platform that is right for their needs?