Blog

IntelinAir shares how they were able to build pixel-accurate training data at quality levels and speeds unachievable with their previous tooling.

In this article, we cover the results of applying different Active Learning methods for semantic segmentation, integration to our platform, share the code and some benchmarking data.

In this article, we cover the results of using the “Learning Loss for Active Learning“ algorithm for object detection and human pose estimation tasks.

In this article, we present our implementation of 2 active learning algorithms, their usage in SuperAnnotate's platform, share the code and some benchmarking data.

Altris AI compared SuperAnnotate and Labelbox and shared their experience on how SuperAnnotate decreased their annotation cost and drastically increased their annotation quality.

Learn how researchers at the TACK project are using SuperAnnotate to speed up image annotation of their project.

Learn how Percepto was able to implement the scalable annotation infrastructure they were looking for, find high-quality outsourced annotation teams, and get lightning-fast annotations all by using SuperAnnotate.

Learn about the ways SuperAnnotate makes managing annotation teams significantly easier, and how our methods can be applied to the Covid-19 world.

In this article, we discuss automation tools within the SuperAnnotate platform that speed up the quality assurance process substantially.

Overview of the advantages and disadvantages of various annotation types. Guidelines on how to speed up pixel-accurate annotations with novel approaches.