Learn what ROI Our Customers Got From Using Lightly

"We had millions of images but no clear way to prioritize. Manual selection was slow and full of guesswork. With Lightly, we just feed in the data and get back what’s actually worth labeling."

We collect millions of road surface images, but since surface imagery is highly spatially correlated, labelling every sample is redundant, and finding sets of diverse data was a challenge.

“The pretrained models were low in performance. The color scheme is probably the reason, they just don’t transfer well to ash-RGB. This is why we decided to give LightlyTrain distillation a try.”

"The Vision Transformer backbones performed far better on our datasets, so we went with DINOv3 even with some tradeoffs - quality is what our clients care about."

"Through this collaboration, SDSC and Lightly have combined their expertise to revolutionize the process of frame selection in surgical videos, making it more efficient and accurate than ever before to find the best subset of frames for labeling and model training."

“Lightly enabled us to improve our ML data pipeline in all regards: Selection, Efficiency, and Functionality. This allowed us to cut customer onboarding time by 50% while achieving better model performance.”