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.
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.
“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.”
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Create ground truth by manually labeling images for training, hyperparameter tuning, and performance estimation.
Export your existing or newly labeled ground-truth dataset to the Superb AI suite.
Create your custom model and
auto-labeling pipeline in less than an hour using your exported data.
Label your remaining data at scale automatically using the custom model you just created.
Instantly see what needs reviewing with AI that highlights annotations with high uncertainty values.
Audit results and repeat the cycle once or twice for edge scenarios using your new labeled dataset as ground truth.
CAL 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.
Proprietary models can be quickly tailored to your unique data, application, or domain using a limited ground truth dataset.
Models are pre-trained on all major data domains, including unconventional or niche data such as satellite, microscope, and computer graphic images.
Identifies hard examples for model training, enabling active learning workflows, and speeding up iteration loops.
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.
