# LightlyTrain Documentation
```{eval-rst}
.. image:: _static/lightly_train_light.svg
:align: center
:class: only-light
.. image:: _static/lightly_train_dark.svg
:align: center
:class: only-dark
```
[](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/object_detection.ipynb)
[](https://docs.lightly.ai/train/stable/installation.html)
[](https://docs.lightly.ai/train/stable/installation.html)
[](https://docs.lightly.ai/train/stable/docker.html#)
[](https://docs.lightly.ai/train/stable/)
[](https://discord.gg/xvNJW94)
*Train Better Models, Faster*
LightlyTrain is the leading framework for transforming your data into state-of-the-art
computer vision models. It covers the entire model development lifecycle from
pretraining DINOv2/v3 vision foundation models on your unlabeled data to fine-tuning
transformer and YOLO models on detection and segmentation tasks for edge deployment.
[Contact us](https://www.lightly.ai/contact) to request a license for commercial use.
## News
- \[[0.14.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-14-0)\] -
2026-01-19: 🐣 **PicoDet, Tiny Models, and ONNX/TensorRT FP16 Support:** PicoDet object
detection models for low-power embedded devices! All tasks now support tiny DINOv3
models and ONNX/TensorRT export in FP16 precision for faster inference! 🐣
- \[[0.13.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-13-0)\] -
2025-12-15: 🐥 **New Tiny Object Detection Models:** We release tiny DINOv3 models
pretrained on COCO for
[object detection](https://docs.lightly.ai/train/stable/object_detection.html#coco)! 🐥
- \[[0.12.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-12-0)\] -
2025-11-06: 💡 **New DINOv3 Object Detection:** Run inference or fine-tune DINOv3
models for
[object detection](https://docs.lightly.ai/train/stable/object_detection.html)! 💡
- \[[0.11.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-11-0)\] -
2025-08-15: 🚀 **New DINOv3 Support:** Pretrain your own model with
[distillation](https://docs.lightly.ai/train/stable/pretrain_distill/methods/distillation.html#methods-distillation-dinov3)
from DINOv3 weights. Or fine-tune our SOTA
[EoMT semantic segmentation model](https://docs.lightly.ai/train/stable/semantic_segmentation.html#semantic-segmentation-eomt-dinov3)
with a DINOv3 backbone! 🚀
- \[[0.10.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-10-0)\] -
2025-08-04: 🔥 **Train state-of-the-art semantic segmentation models** with our new
[**DINOv2 semantic segmentation**](https://docs.lightly.ai/train/stable/semantic_segmentation.html)
fine-tuning method! 🔥
- \[[0.9.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-9-0)\] -
2025-07-21:
[**DINOv2 pretraining**](https://docs.lightly.ai/train/stable/pretrain_distill/methods/dinov2.html)
is now officially available!
## Workflows
````{grid} 1 1 2 3
```{grid-item-card} Object Detection
:link: object_detection.html
Train LTDETR detection models with DINOv2 or DINOv3 backbones.
```
```{grid-item-card} Instance Segmentation
:link: instance_segmentation.html
Train EoMT segmentation models with DINOv3 backbones.
```
```{grid-item-card} Panoptic Segmentation
:link: panoptic_segmentation.html
Train EoMT segmentation models with DINOv3 backbones.
```
```{grid-item-card} Semantic Segmentation
:link: semantic_segmentation.html
Train EoMT segmentation models with DINOv2 or DINOv3 backbones.
```
```{grid-item-card} Image Classification
:link: image_classification.html
Train image classification models with any backbone.
```
```{grid-item-card} Distillation
:link: pretrain_distill/methods/distillation.html
Distill knowledge from DINOv2 or DINOv3 into any model architecture.
```
```{grid-item-card} Pretraining
:link: pretrain_distill/methods/dinov2.html
Pretrain DINOv2 foundation models on your domain data.
```
```{grid-item-card} Autolabeling
:link: predict_autolabel.html
Generate high-quality pseudo labels for detection and segmentation tasks.
```
````
## How It Works [](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/object_detection.ipynb)
Install Lightly**Train** on Python 3.8+ for Windows, Linux or MacOS.
```bash
pip install lightly-train
```
Then train an object detection model with:
```python
import lightly_train
if __name__ == "__main__":
lightly_train.train_object_detection(
out="out/my_experiment",
model="dinov3/vitt16-ltdetr-coco",
data={
# ... Data configuration
}
)
```
And run inference like this:
```python
import lightly_train
# Load the model from the best checkpoint
model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt")
# Or load one of the models hosted by LightlyTrain
model = lightly_train.load_model("dinov3/vitt16-ltdetr-coco")
results = model.predict("image.jpg")
```
See the full [quick start guide](quick-start-object-detection) for more details.
## Features
- Python, Command Line, and [Docker](https://docs.lightly.ai/train/stable/docker.html)
support
- Built for
[high performance](https://docs.lightly.ai/train/stable/performance/index.html)
including [multi-GPU](https://docs.lightly.ai/train/stable/performance/multi_gpu.html)
and [multi-node](https://docs.lightly.ai/train/stable/performance/multi_node.html)
support
- [Monitor training progress](https://docs.lightly.ai/train/stable/pretrain_distill.html#logging)
with MLflow, TensorBoard, Weights & Biases, and more
- Runs fully on-premises with no API authentication
- Export models in their native format for fine-tuning or inference
- Export models in ONNX or TensorRT format for edge deployment
## Models
LightlyTrain supports the following model and workflow combinations.
### Fine-tuning
| Model | Object
Detection | Instance
Segmentation | Panoptic
Segmentation | Semantic
Segmentation | Image
Classification |
| ------ | :-----------------------------------------------------------------: | :----------------------------------------------------------------------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: |
| DINOv3 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/instance_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/panoptic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html#use-eomt-with-dinov3) | ✅ [🔗](https://docs.lightly.ai/train/stable/image_classification.html) |
| DINOv2 | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/instance_segmentation.html) | | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/image_classification.html) |
| Any | | | | | ✅ [🔗](https://docs.lightly.ai/train/stable/image_classification.html) |
### Distillation & Pretraining
| Model | Distillation | Pretraining |
| ------------------------------------------ | :----------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------: |
| DINOv3 | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/methods/distillation.html#distill-from-dinov3) | |
| DINOv2 | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/methods/distillation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/methods/dinov2.html) |
| Torchvision ResNet, ConvNext, ShuffleNetV2 | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/torchvision.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/torchvision.html) |
| TIMM models | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/timm.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/timm.html) |
| Ultralytics YOLOv5–YOLO12, RT-DETR | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/ultralytics.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/ultralytics.html) |
| RT-DETR, RT-DETRv2 | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/rtdetr.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/rtdetr.html) |
| RF-DETR | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/rfdetr.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/rfdetr.html) |
| YOLOv12 | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/yolov12.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/yolov12.html) |
| Custom PyTorch Model | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/custom_models.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/pretrain_distill/models/custom_models.html) |
[Contact us](https://www.lightly.ai/contact) if you need support for additional models.
## Usage Events
LightlyTrain collects anonymous usage events to help us improve the product. We only
track training method, model architecture, and system information (OS, GPU). To opt-out,
set the environment variable: `export LIGHTLY_TRAIN_EVENTS_DISABLED=1`
## License
Lightly**Train** offers flexible licensing options to suit your specific needs:
- **AGPL-3.0 License**: Perfect for open-source projects, academic research, and
community contributions. Share your innovations with the world while benefiting from
community improvements.
- **Commercial License**: Ideal for businesses and organizations that need proprietary
development freedom. Enjoy all the benefits of LightlyTrain while keeping your code
and models private. Includes model training and runtime license.
- **Free Community License**: Available for students, researchers, startups in early
stages, or anyone exploring or experimenting with LightlyTrain. Empower the next
generation of innovators with full access to the world of pretraining.
We're committed to supporting both open-source and commercial users.
[Contact us](https://www.lightly.ai/contact) to discuss the best licensing option for
your project!
## Contact
[](https://www.lightly.ai/lightly-train)
[](https://discord.gg/xvNJW94)
[](https://github.com/lightly-ai/lightly-train)
[](https://x.com/lightlyai)
[](https://www.linkedin.com/company/lightly-tech)
```{toctree}
---
hidden:
maxdepth: 2
---
quick_start_object_detection
quick_start_distillation
installation
object_detection
instance_segmentation
panoptic_segmentation
semantic_segmentation
image_classification
pretrain_distill/index
predict_autolabel
embed
Settings
data/index
performance/index
docker
tutorials/index
python_api/index
faq
changelog
```
