Experience revolutionary efficiency in your labeling workflows with AI-powered automation
Segment anything faster
Create polygon annotations with just one click
Segment any object or background, even those you’ve never labeled, without additional training. Based on the Segment Anything Model, Auto-Edit makes annotating complex or irregular shapes as easy as selecting a region of interest and refining model predictions - all in seconds with minimal human intervention.
What is interactive AI and Auto-Edit?
Manual segmentation of objects using traditional tools like pens and brushes can be significantly time-consuming. Interactive AI is the application of AI directly within the annotation process to assist humans. Auto-Edit is one such application that takes a simple prompt, like clicking a point or drawing a bounding box, and converts it into a polygon.
Content Writer | 3 min read
Label entire datasets in one go
Create Auto-Label models optimized for your project
Leverage AI to quickly, efficiently, and accurately label vast quantities of data for you. Powered by Bayesian deep learning, few-shot learning, transfer learning, and more, auto-label can tackle even the most niche and uncommon objects with minimal ground truth data and training iterations.
Effortlessly annotate moving objects
Automatically label moving objects of varying speeds between frames with interpolation
Accelerate your efforts and eliminate the tedium of annotating every frame or sequence with interpolation-based automation, no training required. Simply label an object of interest across two frames and use interpolation to fill in the gaps, or select multiple key frames to easily tackle objects moving at varying speeds.
Generate descriptive captions with ease
Create rich and accurate captions in a customizable format
Automatically generate text captions that describe your data without any of the labor, time, or cost. Using caption generation, SAM-based detection, and visual question-answer models, AI creates detailed descriptions of objects and scenes, summarizes and organizes them into captions, and provides custom outputs.
Don’t let bad data hold you back
Improve and maintain label quality with mislabel detection
Automatically surface potential errors or misclassifications using advanced detection algorithms that calculate mislabel probability by comparing a target dataset to ground truth. Filter by likelihood, add issue type, and assign to your team to fix - all in a fraction of the time compared to manual review.
Always know what data to mine
Uncover valuable data for model training with uncertainty estimation and active learning
Using a hybrid combination of Monte-Carlo and Uncertainty Distribution Modeling methods, auto-label AI produces image-level difficulty and annotation-level uncertainty values. These identify hard examples near the decision boundary to prioritize when collecting and labeling data for future iteration loops.
Active Learning with Superb AI
When building your model, images that are seemingly common and easy to understand are necessary to establish familiarity, but they do little to nothing to improve your model. On the other hand, images classified as either hard or medium help showcase where the model needs improvement and which examples can lead to better performance.