Labeling. Training. Results on day one.
Faster and smarter labeling
Annotate video data faster using custom predictive labeling and AI-powered frame selection. Unlimited labels with every plan.
Faster time to a model
Continuous model training and a shared, managed dataset allow annotators and data scientists to collaborate and create a functional object detection model in an hour.
Analyze video at scale
Automated data preparation allows you to drag and drop 10 hours of video into a single project. No data curation needed and multiple video formats supported.
Under the hood of our deep learning platform.
Everything you need to label a high-quality video dataset. Easily upload and navigate through hours of video or millions of images, manage annotators, create object categories or add custom metadata to annotations.Learn more
AI model training
Design datasets with one-click GPU assignment and an integrated data science platform powered by Jupyter notebooks. Filter annotations down to the pixel level, train different inference models and validate dataset quality.Learn more
Experiment directly on the dataset while your labelers are working in the annotation tool. Stop wasting time creating and keeping track of dataset copies and manually slicing and dicing your data every time you run an experiment – work on a single source of truth.Learn more
Enterprise-grade solution for data science teams.
Built for enterprise, our platform supports the highest data standards, enabling collaboration without losing control of your data.Talk to sales
Industries and use casesTalk to an expert
Industry 4.0 is pushing manufacturers to optimize production efforts to stay efficient and competitive. Whether you plan to upgrade quality control processes, anomaly or defect detection, or automate packaging inspection, computer vision applications can help bring those capabilities to life. Make every step of the supply chain or manufacturing smarter with computer vision by leveraging video data to create custom AI models for your needs.
Development of autonomous vehicles and smart mobility systems is growing at an unprecedented pace. Accurately labeling video data is crucial for adding self-driving capabilities and will ultimately determine the accuracy of the AI model being created. Combining active learning and continuous training capabilities with a human-in-the-loop workflow helps build and validate high-accuracy models for production-ready self-driving systems.
The primary driver for computer vision in healthcare is making faster and more accurate diagnoses than a physician, primarily through precision medical imaging and AI-driven diagnostics. Leverage computer vision to cut costs in care delivery by automating time-consuming and tedious tasks so clinicians can focus on patient care and patient outcomes.
Computer vision applications are disrupting the food and agriculture industry. As cameras, sensors, satellites, and drones are deployed at scale across outdoor and indoor agricultural environments, providing 100%, 24/7 coverage of crops and livestock, remote agronomy and agriculture have become a reality.
Using in-store cameras, roving bots, and other visual inputs, retailers from grocery stores to clothing and electronics stores are leveraging computer vision to improve store operations and profitability. Optimized checkout experiences, automated inventory management, shopping behavior analysis - these are just a few solutions that can be implemented using CV applications.
The pharmaceutical industry increasingly relies on computer vision to provide improved quality control and a safer working environment. Pharma manufacturers were among the first to embrace color machine vision systems, to ensure tablets were placed in the correct locations in blister packs. Companies are looking for more automation, better documentation, quality assurance, regulatory compliance and computer vision applications are here to help.
As digital advertising has grown, so has the measurability of advertising effectiveness, but some categories like OOH and sponsorships, where ad visibility depends on consumers visiting or passing by fixed locations, still trail behind. Computer vision applications can help close the gap and bring higher quality analytics to advertisers so they can efficiently plan their marketing campaigns.