SuperAnnotate

Semantic Segmentation

Accurately label images at the pixel level, enabling detailed scene understanding for applications like autonomous driving and medical image analysis. This feature allows for precise identification of objects and their boundaries.

Instance Segmentation

Differentiate between individual instances of the same object class, providing granular insights for tasks such as robotics and surveillance. This allows AI to understand not just what objects are present, but also how many of each object there are.

Object Detection

Quickly identify and locate objects within images and videos using bounding boxes, powering applications like object tracking and anomaly detection. This is a fundamental feature for many computer vision tasks.

Collaborative Annotation

Enable seamless teamwork with real-time collaboration features, ensuring consistency and efficiency in large-scale annotation projects. This helps teams work together more effectively and reduces errors.

Quality Control

Implement robust quality control workflows to ensure data accuracy and consistency, leading to more reliable AI models. This includes features like consensus scoring and review processes.

Active Learning

Prioritize the most informative data for annotation, reducing annotation costs and accelerating model training. This helps teams focus on the data that will have the biggest impact on model performance.