Convenient, easy to capture edges, cool workflow. SuperAnnotate is very adaptable. All the tools are very reliable and helps a project run smoothly. I love the frequent updates the comfortable shift to a new UI or feature immersion. Their smart segmentation work is awesome and reduces work for semantic-tier labeling more than 50% by my account.

An outstanding solution for image annotation Superannotate provides an online platform that represents a very powerful instrument for image annotation. It allows people to work together on the same project and check the quality of the annotations. Among the tools it provides, the “Smart Segmentation” option is excellent! It guides annotators during the labeling and speeds up the process a lot.

Keywords: Fast, Updates, Reliability SuperAnnotate is fast, have frequent updates, and easy to use. Wholeheartedly recommend this individuals and/or data labeling outfits. Users could literally feel how far the tool has come just months-in use due to the frequency of the updates they deploy. Two of their integral features require special note. The first is the Smart Segmentation tool which helped us speed up our work signfiicantly - particularly useful for geospatial images and other semantic/panoptic segmentation work. The second feature is its workflow, enabling a step-by-step guide for annotators. Its amazing how such a simple tool minimized the time and potential for errors in labeling operations.

Excellent annotation tool I tried the free desktop tool. It works really well! Pleasant, intuitive, fast, it can definitely serve for small to medium size projects, as advertised. The annotations are accurate, the .json format to save them is perfectly appropriate. I'll give it 5 stars when it is possible to annotate videos rather than just images. Great tool anyway! [EDIT September 2021] Video annotation is now available, it really is a perfect tool, 5 stars without doubt!

SuperAnnotate: A great tool for data annotation SuperAnnotate is an awesome tool for the researchers/students who are working with the dataset preparation for computer vision related applications such as environment perception including object detection and semantic segmentation. It helped me a lot in my Masters' thesis. Recommended.