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.

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. :)

How to improve both inference time (2x) and F1 score (by 0.06) of the pruned neural network using knowledge distillation. 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%.