Superb Labeling
Automate Labeling
Enterprise data management and powerful project analytics.
Automate Your Way to Better Datasets
Getting enough quality data for training, testing, and validation is often the hardest part of building computer vision applications. Superb AI provides intelligent automation, known as auto-label and custom auto-label (CAL), that automatically detects and labels common or niche objects in images and videos using highly accurate pre-trained models or a model fine-tuned using your data.
Label your unique datasets at a massive scale - fast
Computer vision has a chicken and egg dilemma. Labeling is labor-intensive, but conventional automation requires heaps of pre-labeled data to train initial models.
But now, you can automatically label large datasets using a custom model fine-tuned to your exact use case - all with limited ground truth and a few clicks using our no-code UI.
Ensure high-quality auto annotations - every time
Conventional labeling algorithms struggle with all but the most identifiable labels, making mislabeling all-too-common.
Without excessive verification of outputs, errors are likely to slip through, proving costly when fed to your models.
Consistency and precision are, however, as important as speed.
Superb AI provides patented uncertainty estimation that immediately highlights hard labels to be audited - speeding up active learning workflows and iteration loops.
Henry Acevedo
Robotics Software Developer at Fox Robotics
“Superb AI’s custom auto-label significantly improves our labeling speed and accuracy. The number of quality labels you can get in a short time is astounding, especially when you consider you only need a few hundred labeled images for ground truth to get started. It’s a better way to label data.”
How it works
Easy to Customize, Refine, and Scale
Reap all the benefits of faster data preparation and cheaper labeling with automation you can trust.
1
Create ground truth by manually labeling images for training, hyperparameter tuning, and performance estimation.
2
Export your existing or newly labeled ground-truth dataset to the Superb AI suite.
3
Create your custom model and auto-labeling pipeline in less than an hour using your exported data.
4
Label your remaining data at scale automatically using the custom model you just created.
5
Instantly see what needs reviewing with AI that highlights annotations with high uncertainty values.
6
Audit results and repeat the cycle once or twice for edge scenarios using your new labeled dataset as ground truth.
Learn how to build the best ground truth dataset and the most accurate auto-label AI with our best practices guide.
What Powers Custom Auto Label
Custom Auto Label combines cutting-edge ML methods, optimized over a set of metrics substantially different from typical ML models focused on external services, with a unique tech stack designed from the ground up for data labeling and QA automation.
Transfer and Few-Shot Learning
Proprietary models can be quickly tailored to your unique data, application, or domain using a limited ground truth dataset.
Self-Supervised Learning
Models are pre-trained on all major data domains, including unconventional or niche data such as satellite, microscope, and computer graphic images.
Bayesian Deep Learning
Identifies hard examples for model training, enabling active learning workflows, and speeding up iteration loops.
Why Custom Auto Label
The time and cost-saving benefits of labeling automation have long tantalized yet eluded most data teams.
Often, you spend more time fixing errors than just labeling your data from scratch, especially for edge cases.
Superb AI provides a new, data-driven approach that makes highly accurate automation accessible to all.
With Custom Auto Label
Auto-label thousands of images and videos at high throughput with a single source of truth (your data).
Leverage intelligent automation that requires minimal verification and can be adapted at will, so your manual labeling efforts can end at creating small ground truth datasets
Create custom models for any use case, including niche or edge-case rich datasets, by training with your unique data - all on the fly.
With conventional approaches
Hand labeling is prohibitively slow and can introduce error and bias to your models as every labeler is different.
AI-assisted labeling can shave a second or two, but you still have to perform basic operations manually while relying on costly human labor.
Pre-trained models often can’t account for the nuances of your data, and soft labels are only good at what your models were already good at.