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Help ML teams label the right data
When ML teams send their data to companies like Scale.ai for labeling, most can only afford to label 1% or less of their datasets. But today they don’t have a good way to pick which 1% to label. We help them pick the best 1% of their data to label. By labeling the most representative data, they significantly improve model accuracy at the same cost.
Lightly provides a suite of computer vision data tools that help ML teams curate, label, manage, and preprocess data for vision models, including edge data collection and pretraining capabilities. It combines data curation, labeling, model pretraining, and deployment support in an integrated platform.
Lightly offers LightlyStudio for curating, labeling, and managing datasets; LightlyTrain for self-supervised pretraining and fine-tuning vision models; LightlyEdge as a smart data selection SDK for edge devices to optimize data collection; and LightlyServices for AI training data services. The platform integrates into existing ML pipelines, enabling data curation at scale, standardized labeling, and edge-enabled data collection to support production-grade model development and deployment.
Who it’s for: ML teams and organizations working on computer vision who need data curation, labeling, pretraining, fine-tuning, and edge data collection for production-ready vision models.
Hiring/traction mentions not explicit in text; product updates and enterprise trust (Fortune100) are noted, indicating active development and market adoption
Igor has more than five years of experience in machine learning. He holds a degree in electrical engineering from ETH Zurich. During his studies, he developed a lot of experience in machine learning and robotics and had multiple successful publications in the area of deep learning at top conferences such as ICML and ECCV. He previously worked for two years at the Swiss stock exchange as a software engineer.
Build better computer vision models by building better datasets
We help ML teams improve their models by improving their datasets
