Blog

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.

The process of annotating thousands of images is time-consuming. Learn how to automate your annotation process using transfer learning techniques.

This article discusses one of the main issues when getting high-quality AI training data. Managed Crowdsourcing Services are becoming more popular because of the ongoing pandemic that forces teams to work from home. This makes it quite difficult for professional service providers to keep the same level of annotation quality and speed they used to provide when employees worked in the office. For computer vision engineers or service companies that manage such teams, we propose multiple techniques which makes the management of such teams fast and efficient without compromising annotation quality.

In this post, I will share some ideas related to image annotation that I accumulated during my PhD research. Specifically, I will discuss the current state-of-the-art annotation methods, their trends, and future directions. Finally, I will briefly talk about the annotation software we are building and give a little preview about our company — SuperAnnotate.