We cover the results of applying different Active Learning methods for semantic segmentation, integration to our platform, the code, and some benchmarking data.

Explore the application of the Learning Loss for Active Learning algorithms in object detection and human pose estimation tasks within this article on Active Learning.

We present our implementation of 2 active learning algorithms, their usage in SuperAnnotate's platform, share the code and some benchmarking data.

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

Explore the automaation tools within the SuperAnnotate Platform that speed up the quality assurance process substantially.

Find out more about pixel-precision in aerial imagery, AI segmentation-based approaches, pixelwise annotation, and more.

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

Check out SuperAnnotate's new updates along with editor improvements and new features including duplicate mode, and approve/disapprove instances.

Scale and automate your annotation process using transfer learning. We share a tutorial on how transfer learning works and how to do it without coding.

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

Use the smart prediction integrated into our platform to accelerate the text annotation process. Our prediction algorithm is able to receive 97% mAP score.

Introducing SuperAnnotate’s U-Net-based building footprint pre-annotation. Follow the link to learn more about the algorithm and code description.