Active Learning for Semantic Segmentation

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.

Active Learning for Object detection and Human Pose estimation

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

Active Learning for classification models

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.

How to effectively manage annotation teams during Covid-19

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

How to Detect 93% of Mislabeled Annotations While Spending 4x Less Time on Quality Assurance

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

Annotations for Aerial Imagery: Why Pixel Precision Will Be the New Norm

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

Speed up image labeling using transfer learning (no code required)

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

AI Annotation During Covid-19

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.

Why pixel precision is the future of the Image Annotation

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.