lightly-train

retrain using your existing unlabeled image data. - Better Performance: Consistently outperforms training from scratch and ImageNet-pretrained weights, especially in low-data regimes and domain-specific tasks (benchmarks in README/blog). We see gains across detection, classification, and segmentation. - Domain Adaptation: Tailor models to your specific industry (manufacturing, healthcare, retail, etc.). - Supports Popular Models: Works out-of-the-box with YOLO (v5-v12), RT-DETR, ResNet, ViTs, etc., integrating with frameworks like Ultralytics, TIMM, Torchvision. - Easy to Use & Scalable: Simple pip install, minimal code to start, scales to millions of images, runs fully on-premise (single/multi-GPU). We built this because while SSL research is mature, making it easily accessible and effective for industry computer vision teams was hard. LightlyTrain aims to bridge that gap. We’ve benchmarked it on COCO, BDD100K (driving), DeepLesion (medical), and DeepWeeds (agriculture), showing strong improvements over baselines (details in the repo/blog post linked below). For example, on COCO with only 10% labels, LightlyTrain pretraining boosted YOLOv8-s mAP by +14% over ImageNet weights and +34% over no pretraining. - GitHub Repo: https://github.com/lightly-ai/lightly-train - Docs: https://docs.lightly.ai/train - Detailed Blog Post/Benchmarks: https://www.lightly.ai/blog/introducing-lightly-train - Quick Demo Video: https://youtu.be/5Lmry1k_cA8 We’re here to answer any questions! Happy to discuss the tech, benchmarks, or use cases. Commercial licenses are also available for businesses needing different terms.