lightly-ai/lightly-train

Health v2 · maintenance only

Adoption not part of the score

1652 stars · 107 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LightlyTrain is a Python framework for training computer vision models, covering pretraining of vision foundation models (DINOv2/v3) on unlabeled data, fine-tuning of transformer and YOLO models for detection and segmentation, and model distillation. It supports ONNX and TensorRT export for edge deployment and is licensed under AGPL-3.0 with a commercial license option.

Use cases

• pretrain vision models on unlabeled images
• fine-tune YOLO for object detection
• train semantic segmentation models
• distill large vision models into smaller ones
• export detection models to ONNX and TensorRT
• train DINOv3 vision transformers on custom data
• train instance segmentation models for edge deployment
• compute image embeddings with self-supervised learning

When to choose

• you need state-of-the-art pretraining or fine-tuning for vision models like YOLO, ViTs, or DETR variants
• you want to leverage unlabeled data via self-supervised pretraining
• you need distillation to shrink models for edge deployment
• you want built-in ONNX/TensorRT export for real-time inference

When to avoid

• you need a permissive open-source license for proprietary commercial products without a commercial license
• you work outside computer vision (e.g., NLP or audio)
• you only need inference with existing pretrained models rather than training

Facets

library · maturity active

machine-learning deep-learning llm-training computer-vision machine-learning deep-learning python cross-platform self-supervised-learning pretraining fine-tuning distillation object-detection semantic-segmentation yolo vision-transformer dinov2 dinov3 pytorch embeddings contrastive-learning onnx-export tensorrt gpu docker

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