LightlyTrain: Better Vision Models, Faster – No Labels Needed

Summary

Train Better Models, Faster - No Labels Needed LightlyTrain brings self-supervised pretraining to real-world computer vision pipelines, using your unlabeled data to reduce labeling costs and speed up model deployment. Leveraging the state-of-the-art from research, it pretrains your model on your unlabeled, domain-specific data, significantly reducing the amount of labeling needed to reach a high model performance. This allows you to focus on new features and domains instead of managing your labeling cycles. LightlyTrain is designed for simple integration into existing training pipelines and supports a wide range of model architectures and use-cases out of the box. Why LightlyTrain 💸 No Labels Required : Speed up development by pretraining models on your unlabeled image and video data. : Speed up development by pretraining models on your unlabeled image and video data. 🔄 Domain Adaptation : Improve models by pretraining on your domain-specific data (e.g. video analytics, agriculture, automotive, healthcare, manufacturing, retail, and more). : Improve models by pretraining on your domain-specific data (e.g. video analytics, agriculture, automotive, healthcare, manufacturing, retail, and more). 🏗️ Model & Task Agnostic : Compatible with any architecture and task, including detection, classification, and segmentation. : Compatible with any architecture and task, including detection, classification, and segmentation. 🚀 Industrial-Scale Support: LightlyTrain scales from thousands to millions of images. Supports on-prem, cloud, single, and multi-GPU setups. On COCO, YOLOv8-s models pretrained with LightlyTrain achieve high performance across all tested label fractions. These improvements hold for other architectures like YOLOv11, RT-DETR, and Faster R-CNN. See our announcement post for more details. How It Works Install LightlyTrain: pip install lightly-train Then start pretraining with: import lightly_train if __name__ == "__main__" : lightly_train . train ( out = "out/my...