Product

Automate

Automatically detect and label common or niche objects in images and videos using highly accurate pre-trained models (auto-label) or a model fine-tuned using your data (custom auto-label).
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Automating Data Preparation for Computer Vision

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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.

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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.

Madhuri Patil
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.”

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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
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Ground truth

Create ground truth by manually labeling images for training, hyperparameter tuning, and performance estimation.

2
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Export

Export your existing or newly labeled ground-truth dataset to the Superb AI suite.

3
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Train

Create your custom model and
auto-labeling pipeline in less than an hour using your exported data.

4
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Auto label

Label your remaining data at scale automatically using the custom model you just created.

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Uncertainty estimation

Instantly see what needs reviewing with AI that highlights annotations with high uncertainty values.

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Audit

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

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.

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Transfer and Few-Shot Learning

Proprietary models can be quickly tailored to your unique data, application, or domain using a limited ground truth dataset.

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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.

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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.

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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.
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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.

Automate Your Way to Better Datasets

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