Mighty AI’s platform helps curate high quality labeled training data for computer vision systems. We provide all the software, expertise, and expert annotators needed to create ground truth datasets. We convert raw, unlabeled data into useful high-quality data, pairing machine learning with human intelligence to revolutionize how companies manage computer vision data and create ground truth datasets to train and validate their models. Visit www.mighty.ai to learn more, and follow us at @mighty_ai.
Mighty AI removes the time-consuming tasks associated with preparing and labelling data for training deep learning systems. Combining Mighty AI's ground truth data management platform with Intel architecture allows you to create a streamlined deep learning workflow, from the ingestion and labeling of training data to the generation and validation of resulting models. Thanks to this end-to-end workflow, you'll be able to speed up your deployments and use of deep learning.
In this white paper, explore topics such as when to optimize for real data and when to optimize for synthetic data; ways to achieve high quality standards in your data labeling programs; and best practices for capturing real data from on-road driving.
In this podcast, Mighty AI dives into challenges faced by people that need to label training data and how to develop a cohesive system for performing the various labeling tasks they’re likely to encounter.
Listen to this podcast to learn more about the many challenges of collecting training data for autonomous vehicles, along with some thoughts on human-powered insights and annotation, semantic segmentation, the need for diverse data from around the world, and more.
Mighty AI and Mcity are teaming up to provide a new vehicle and pedestrian detection training dataset exclusively for Mcity members to train their machine learning models.
This archive contains CSV- and JSON-formatted polygon segment data and 200 fully segmented frames from dashcam stills captured in the Seattle, WA USA metro area in 2017. The annotated source frames are 1620×1080.