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For most industries, like autonomous driving, security, and surveillance, infrastructure monitoring, etc. the data that needs to be annotated is collected via cameras. In the annotation stage, one would normally have to pre-process their data to fit the format of most image annotation platforms.

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.

At Annotate.Online we develop new features, integrate new functionalities, improve existing tools, and release the changes regularly at the end of each development cycle. In this article, you will find all the updates on the current cycle. At this stage, we are releasing a complete set of dashboards for an effective project, user, and budget management. Here we go! :)

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.

An annotation automation platform by SuperAnnotate: designed for researchers, by researchers In this article, I would like to present our new annotation platform (annotate.online), share its story and explain why we decided to provide the platform to the researchers for free.