Labelbox

"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.”

"It took far less work than expected to plug DINO into our SSL system - the LightlySSL code was clean and easy to adapt"

One of our main constraints is that we don't have millions of labeled images, just a small amount. Using LightlyTrain to train a Vision Transformer backbone like DINOv2 or DINOv3 gets us way more efficient training results even with less data.

“By integrating Lightly into our existing workflow, we achieved a 90% reduction in dataset size and doubled the efficiency of our deployment process. The tool’s seamless implementation significantly enhanced our data pipeline.”

“Lightly gave us transparency to a part of the ML development that is a black box, data. Furthermore, Lightly enabled us to do Active Learning at scale and helped us improve recall and F1-score of our object detector by 32% and 10% compared to our previous data selection method. We finally saw the light in our data using Lightly.”

"Lightly is hyper-focused on finding thousands of relevant images from millions of video frames to improve deep learning models. The Lightly platform enabled us to build models and deploy features more than 2x faster and unlock completely new development workflows."

"I was truly amazed once we received the results of Lightly. We knew we had a lot of similar images due to our video feed but the results showed us how we can work more efficiently by selecting the right data"

"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.”

"It took far less work than expected to plug DINO into our SSL system - the LightlySSL code was clean and easy to adapt"

One of our main constraints is that we don't have millions of labeled images, just a small amount. Using LightlyTrain to train a Vision Transformer backbone like DINOv2 or DINOv3 gets us way more efficient training results even with less data.

“By integrating Lightly into our existing workflow, we achieved a 90% reduction in dataset size and doubled the efficiency of our deployment process. The tool’s seamless implementation significantly enhanced our data pipeline.”

“Lightly gave us transparency to a part of the ML development that is a black box, data. Furthermore, Lightly enabled us to do Active Learning at scale and helped us improve recall and F1-score of our object detector by 32% and 10% compared to our previous data selection method. We finally saw the light in our data using Lightly.”

"Lightly is hyper-focused on finding thousands of relevant images from millions of video frames to improve deep learning models. The Lightly platform enabled us to build models and deploy features more than 2x faster and unlock completely new development workflows."

"I was truly amazed once we received the results of Lightly. We knew we had a lot of similar images due to our video feed but the results showed us how we can work more efficiently by selecting the right data"