23 December 2020
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.
1 December 2020
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.
17 November 2020
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.
2 October 2020
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.
28 September 2020
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.
23 September 2020
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.
1 September 2020
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.
15 July 2020
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.
22 June 2020
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.