Blog

As part of our partnership with OpenCV, we are launching the best free annotation tool for the computer vision community.

We’ve been super busy at SuperAnnotate these past few weeks, and we’re excited to share with you our new features and updates.

This version of AnnotateOnline updates will include several editor improvements and new features. In this update report, you will find: Duplicate mode Upload pre-annotation in Pixelwise editor Approve / Disapprove instances Image performance improvements/Autosave

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

Whether you need an invoice annotation for a quick expense report, professional accounting project, or extensive financial analyses, this particular type of text annotation task can be complex and time-consuming. With all the available photo and scanning technologies, it has become much easier to collect receipt scans, however, most of the annotation tools don't seem to catch up with such a speed. Without the possibility of labeling your data as effectively as possible, the time gained on data collection will be inevitably lost on document annotation.

In this article, I would like to present our new building pre-annotation for aerial images of annotate.online platform, share it’s code and algorithm and the motivation of integrating it into our platform.

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 the integration of the entropy value, the approve and disapprove functionalities for QA, and higher-level customer supervision as well as sign up, export, and image tab improvements. :)

In this article, I will share my experience with shrinking contour/edge detection models. I will describe in detail model architectures and training experiments that lead to a 6x faster network that underperforms SOTA models by only ~2%.

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.