# lightly-train **Repository Path**: soon14/lightly-train ## Basic Information - **Project Name**: lightly-train - **Description**: https://github.com/lightly-ai/lightly-train - **Primary Language**: Python - **License**: AGPL-3.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-07-20 - **Last Updated**: 2026-07-20 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
LightlyTrain

SOTA Pretraining, Fine-tuning and Distillation

Train Better Models, Faster

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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. Struggling to get good results with pre-training? Talk to one of our experts [Contact us](https://www.lightly.ai/contact) Using LightlyTrain at work, in production, on the edge, or to build proprietary models? You likely need a Commercial License. [Contact us](https://www.lightly.ai/contact) to request a license for commercial use. Also check out [LightlyStudio](https://github.com/lightly-ai/lightly-studio) to easily visualize your annotations and predictions. ## News - \[[0.16.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-16-0)\] - 2026-06-25: ⚡ **Upgraded LTDETRv2 for object detection:** Following the success of LTDETR, LightlyTrain's DETR model, we release LTDETRv2 with significant architectural and performance improvements! It supports using ECViT backbones from [EdgeCrafter](https://arxiv.org/abs/2603.18739) and ONNX/TensorRT export for faster inference! - \[[0.15.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-15-0)\] - 2026-04-14: 🔎 **Distillationv3:** Better generalizing distillation method that performs equally well across dense and global tasks and across all models, from ViTs to hybrids to CNNs (+support for custom teachers!). 🔎 - \[[0.14.0](https://docs.lightly.ai/train/stable/changelog.html#changelog-0-14-0)\] - 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) 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! ## Installation Install Lightly**Train** on Python 3.8+ for Windows, Linux or MacOS with: ```bash pip install lightly-train ``` ## Workflows ![Tasks](docs/source/_static/images/tasks/tasks.png)
Object Detection Train LTDETR detection models with DINOv2, DINOv3, or EdgeCrafter ECViT backbones. #### COCO Results | Model | Val mAP50:95 | Latency (ms) | Params (M) | Input Size | | :-------------------------------: | :---------------------: | :----------: | :--------: | :---------: | | picodet-s-coco | 26.7\* | 2.2\* | 1.17 | 416×416 | | picodet-l-coco | 32.0\* | 2.4\* | 3.75 | 416×416 | | **ltdetrv2-s-coco (NEW)** | **50.7** | **5.4** | **9.9** | **640×640** | | dinov3/vitt16-ltdetr-coco | 49.8 | 5.4 | 10.1 | 640×640 | | dinov3/vitt16plus-ltdetr-coco | 52.5 | 7.0 | 18.1 | 640×640 | | dinov3/vits16-ltdetr-coco | 55.4 | 10.5 | 36.4 | 640×640 | | dinov3/convnext-tiny-ltdetr-coco | 54.4 | 13.3 | 61.1 | 640×640 | | dinov3/convnext-small-ltdetr-coco | 56.9 | 17.7 | 82.7 | 640×640 | | dinov3/convnext-base-ltdetr-coco | 58.6 | 24.7 | 121.0 | 640×640 | | dinov3/convnext-large-ltdetr-coco | 60.0 | 42.3 | 230.0 | 640×640 | \*Picodet models are in preview and we report preliminary results. Models are trained on the COCO 2017 dataset and evaluated on the validation set with single-scale testing. Latency is measured with TensorRT on a NVIDIA T4 GPU with batch size 1. All models are optimized using `tensorrt==10.13.3.9`. #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/object_detection.html) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/object_detection.ipynb) ```python import lightly_train if __name__ == "__main__": # Train with our most recent LT-DETRv2 detector based on DINOv3 and EdgeCrafter. lightly_train.train_object_detection( out="out/my_experiment", model="ltdetrv2-s-coco", data={ "path": "my_data_dir", "train": "images/train", "val": "images/val", "names": { 0: "person", 1: "bicycle", 2: "car", # ... }, }, ) # Load model and run inference model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt") # Or use one of the models provided by LightlyTrain # model = lightly_train.load_model("ltdetrv2-s-coco") results = model.predict("image.jpg") results["labels"] # Class labels, tensor of shape (num_boxes,) results["bboxes"] # Bounding boxes in (xmin, ymin, xmax, ymax) absolute pixel # coordinates of the original image. Tensor of shape (num_boxes, 4). results["scores"] # Confidence scores, tensor of shape (num_boxes,) ```
Panoptic Segmentation Train state-of-the-art panoptic segmentation models with DINOv3 backbones using the EoMT method from CVPR 2025. #### COCO Results | Implementation | Model | Val PQ | Avg. Latency (ms) | Params (M) | Input Size | | ------------------------------------ | ------------------------------------- | -------- | ----------------- | ---------- | ---------- | | LightlyTrain | dinov3/vitt16-eomt-panoptic-coco | 38.0 | 13.5 | 6.0 | 640×640 | | LightlyTrain | dinov3/vittplus16-eomt-panoptic-coco | 41.4 | 14.1 | 7.7 | 640×640 | | LightlyTrain | dinov3/vits16-eomt-panoptic-coco | 46.8 | 21.2 | 23.4 | 640×640 | | LightlyTrain | dinov3/vitb16-eomt-panoptic-coco | 53.2 | 39.4 | 92.5 | 640×640 | | LightlyTrain | dinov3/vitl16-eomt-panoptic-coco | 57.0 | 80.1 | 315.1 | 640×640 | | LightlyTrain | dinov3/vitl16-eomt-panoptic-coco-1280 | **59.0** | 500.1 | 315.1 | 1280×1280 | | EoMT (CVPR 2025 paper, current SOTA) | dinov3/vitl16-eomt-panoptic-coco-1280 | 58.9 | - | 315.1 | 1280×1280 | Tiny models are trained for 48 epochs, small and base models for 24 epochs and large models for 12 epochs on the COCO 2017 dataset and evaluated on the validation set with single-scale testing. Avg. Latency is measured on a single NVIDIA T4 GPU with batch size 1\. All models are optimized using `torch.compile`. #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/panoptic_segmentation.html) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/eomt_panoptic_segmentation.ipynb) ```python import lightly_train if __name__ == "__main__": # Train an panoptic segmentation model with a DINOv3 backbone lightly_train.train_panoptic_segmentation( out="out/my_experiment", model="dinov3/vitb16-eomt-panoptic-coco", data={ "train": { "images": "images/train", "masks": "annotations/train", "annotations": "annotations/train.json", }, "val": { "images": "images/val", "masks": "annotations/val", "annotations": "annotations/val.json", }, }, ) model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt") results = model.predict("image.jpg") results["masks"] # Masks with (class_label, segment_id) for each pixel, tensor of # shape (height, width, 2). Height and width correspond to the # original image size. results["segment_ids"] # Segment ids, tensor of shape (num_segments,). results["scores"] # Confidence scores, tensor of shape (num_segments,) ```
Instance Segmentation Train state-of-the-art instance segmentation models with DINOv3 backbones using the EoMT method from CVPR 2025. #### COCO Results | Implementation | Model | Val mAP mask | Avg. Latency (ms) | Params (M) | Input Size | | ------------------------------------ | -------------------------------- | ------------ | ----------------- | ---------- | ---------- | | LightlyTrain | dinov3/vitt16-eomt-inst-coco | 25.4 | 12.7 | 6.0 | 640×640 | | LightlyTrain | dinov3/vitt16plus-eomt-inst-coco | 27.6 | 13.3 | 7.7 | 640×640 | | LightlyTrain | dinov3/vits16-eomt-inst-coco | 32.6 | 19.4 | 21.6 | 640×640 | | LightlyTrain | dinov3/vitb16-eomt-inst-coco | 40.3 | 39.7 | 85.7 | 640×640 | | LightlyTrain | dinov3/vitl16-eomt-inst-coco | **46.2** | 80.0 | 303.2 | 640×640 | | EoMT (CVPR 2025 paper, current SOTA) | dinov3/vitl16-eomt-inst-coco | 45.9 | - | 303.2 | 640×640 | Tiny models are trained for 48 epochs, while all other models are trained for 12 epochs on the COCO 2017 dataset and evaluated on the validation set with single-scale testing. Average latency is measured on a single NVIDIA T4 GPU with batch size 1. All models are optimized using `torch.compile`. #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/instance_segmentation.html) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/eomt_instance_segmentation.ipynb) ```python import lightly_train if __name__ == "__main__": # Train an instance segmentation model with a DINOv3 backbone lightly_train.train_instance_segmentation( out="out/my_experiment", model="dinov3/vitb16-eomt-inst-coco", data={ "path": "my_data_dir", "train": "images/train", "val": "images/val", "names": { 0: "background", 1: "vehicle", 2: "pedestrian", # ... }, }, ) model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt") results = model.predict("image.jpg") results["labels"] # Class labels, tensor of shape (num_instances,) results["masks"] # Binary masks, tensor of shape (num_instances, height, width). # Height and width correspond to the original image size. results["scores"] # Confidence scores, tensor of shape (num_instances,) ```
Semantic Segmentation Train state-of-the-art semantic segmentation models with DINOv2 or DINOv3 backbones using the EoMT method from CVPR 2025. #### COCO-Stuff Results | Implementation | Model | Val mIoU | Avg. Latency (ms) | Params (M) | Input Size | | -------------- | --------------------------- | -------- | ----------------- | ---------- | ---------- | | LightlyTrain | dinov3/vitt32-eomt-coco | 34.0 | 4.2 | 6.0 | 512×512 | | LightlyTrain | dinov3/vitt32plus-eomt-coco | 36.0 | 4.4 | 7.7 | 512×512 | | LightlyTrain | dinov3/vits32-eomt-coco | 42.4 | 5.4 | 21.6 | 512×512 | | LightlyTrain | dinov3/vitb32-eomt-coco | 48.3 | 9.4 | 85.7 | 512×512 | | LightlyTrain | dinov3/vitl32-eomt-coco | 51.2 | 17.5 | 303.2 | 512×512 | | LightlyTrain | dinov3/vitt16-eomt-coco | 37.9 | 6.0 | 6.0 | 512×512 | | LightlyTrain | dinov3/vitt16plus-eomt-coco | 39.5 | 6.4 | 7.7 | 512×512 | | LightlyTrain | dinov3/vits16-eomt-coco | 45.0 | 11.3 | 21.6 | 512×512 | | LightlyTrain | dinov3/vitb16-eomt-coco | 50.1 | 23.1 | 85.7 | 512×512 | | LightlyTrain | dinov3/vitl16-eomt-coco | **52.5** | 49.0 | 303.2 | 512×512 | Models are trained for 12 epochs with `num_queries=200` on the COCO-Stuff dataset and evaluated on the validation set with single-scale testing. Average latency is measured on a single NVIDIA T4 GPU with batch size 1. All models optimized using `torch.compile`. #### Cityscapes Results | Implementation | Model | Val mIoU | Avg. Latency (ms) | Params (M) | Input Size | | :----------------------------------: | :---------------------------: | :------: | :---------------: | :--------: | :--------: | | LightlyTrain | dinov3/vits16-eomt-cityscapes | 78.6 | 53.8 | 21.6 | 1024×1024 | | LightlyTrain | dinov3/vitb16-eomt-cityscapes | 81.0 | 114.9 | 85.7 | 1024×1024 | | LightlyTrain | dinov3/vitl16-eomt-cityscapes | **84.4** | 256.4 | 303.2 | 1024×1024 | | EoMT (CVPR 2025 paper, current SOTA) | dinov2/vitl16-eomt | 84.2 | - | 319 | 1024×1024 | Average latency is measured on a single NVIDIA T4 GPU with batch size 1. All models are optimized using `torch.compile`. #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/semantic_segmentation.html) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/eomt_semantic_segmentation.ipynb) ```python import lightly_train if __name__ == "__main__": # Train a semantic segmentation model with a DINOv3 backbone lightly_train.train_semantic_segmentation( out="out/my_experiment", model="dinov3/vits16-eomt", data={ "train": { "images": "my_data_dir/train/images", "masks": "my_data_dir/train/masks", }, "val": { "images": "my_data_dir/val/images", "masks": "my_data_dir/val/masks", }, "classes": { 0: "background", 1: "road", 2: "building", # ... }, }, ) # Load model and run inference model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt") # Or use one of the models provided by LightlyTrain # model = lightly_train.load_model("dinov3/vits16-eomt") masks = model.predict("image.jpg") # Masks is a tensor of shape (height, width) with class labels as values. # It has the same height and width as the input image. ```
Image Classification Train multiclass or multilabel image classification models with any backbone. #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/image_classification.html) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/image_classification.ipynb) ```python import lightly_train if __name__ == "__main__": # Train an image classification model with a DINOv3 backbone lightly_train.train_image_classification( out="out/my_experiment", model="dinov3/vitt16", data={ "train": "my_data_dir/train/", "val": "my_data_dir/val/", "classes": { 0: "cat", 1: "car", 2: "dog", # ... }, }, ) model = lightly_train.load_model("out/my_experiment/exported_models/exported_best.pt") results = model.predict("image.jpg", topk=1, threshold=0.5) results["labels"] # Class labels, tensor of shape (topk,) results["scores"] # Confidence scores, tensor of shape (topk,) ```
Depth Estimation Run monocular depth inference with Depth Anything V2 and V3 models. Training support will be released soon! #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/depth_estimation.html) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/depth_estimation.ipynb) ```python import lightly_train # Load a depth model provided by LightlyTrain model = lightly_train.load_model("dinov2/dav3-relative-large") # Predict a relative-depth map depth = model.predict("image.jpg") # depth is a tensor of shape (height, width) matching the input image. ``` Metric depth (in meters) and the full list of available models are covered in the [documentation](https://docs.lightly.ai/train/stable/depth_estimation.html).
Distillation (DINOv2/v3) Pretrain any model architecture with unlabeled data by distilling the knowledge from DINOv2 or DINOv3 foundation models into your model. On the COCO dataset, 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](https://www.lightly.ai/blog/introducing-lightly-train) for more benchmarks and details. ![Benchmark Results](https://cdn.prod.website-files.com/62cd5ce03261cb3e98188470/67fe4efa0209fb4eb0c3da5c_Introducing%20LightlyTrain_imag_1.png) #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/pretrain_distill/methods/distillation.html) [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lightly-ai/lightly-train/blob/main/examples/notebooks/distillation.ipynb) ```python import lightly_train if __name__ == "__main__": # Distill the knowledge from a DINOv3 teacher into a YOLOv8 model lightly_train.pretrain( out="out/my_experiment", data="my_data_dir", model="ultralytics/yolov8s", method="distillation", method_args={ "teacher": "dinov3/vitb16", }, ) # Load model for fine-tuning model = YOLO("out/my_experiment/exported_models/exported_last.pt") model.train(data="coco8.yaml") ```
Pretraining (DINOv2 Foundation Models) With LightlyTrain you can train your very own foundation model like DINOv2 on your data. #### ImageNet-1K Results | Implementation | Model | Val ImageNet k-NN | | :------------: | :-----------: | :---------------: | | LightlyTrain | dinov2/vitl16 | **81.9%** | | DINOv2 | dinov2/vitl16 | 81.6% | Models are pretrained on ImageNet-1k for 100 epochs and evaluated with a k-NN classifier on the ImageNet validation set. #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/pretrain_distill/methods/dinov2.html) ```python import lightly_train if __name__ == "__main__": # Pretrain a DINOv2 vision foundation model lightly_train.pretrain( out="out/my_experiment", data="my_data_dir", model="dinov2/vitb14", method="dinov2", ) ```
Autolabeling LightlyTrain provides simple commands to autolabel your unlabeled data using DINOv2 or DINOv3 pretrained models. This allows you to efficiently boost performance of your smaller models by leveraging all your unlabeled images. #### ADE20K Results | Implementation | Model | Autolabel | Val mIoU | Params (M) | Input Size | | :------------: | :-----------------------: | :-------: | :-------: | :--------: | :--------: | | LightlyTrain | dinov3/vits16-eomt | ❌ | 0.466 | 21.6 | 518×518 | | LightlyTrain | dinov3/vits16-eomt-ade20k | ✅ | **0.533** | 21.6 | 518×518 | | LightlyTrain | dinov3/vitb16-eomt | ❌ | 0.544 | 85.7 | 518×518 | | LightlyTrain | dinov3/vitb16-eomt-ade20k | ✅ | **0.573** | 85.7 | 518×518 | The better results with auto-labeling were achieved by fine-tuning a ViT-H+ on the ADE20K dataset, which reaches 0.595 validation mIoU. This model was then used to autolabel 100k images from the SUN397 dataset. Using these labels, we subsequently fine-tuned the smaller models, and then used the ADE20k dataset for validation. #### Usage [![Documentation](https://img.shields.io/badge/Documentation-blue)](https://docs.lightly.ai/train/stable/predict_autolabel.html) ```python import lightly_train if __name__ == "__main__": # Autolabel your data with a DINOv3 semantic segmentation model lightly_train.predict_semantic_segmentation( out="out/my_autolabeled_data", data="my_data_dir", model="dinov3/vitb16-eomt-coco", # Or use one of your own model checkpoints # model="out/my_experiment/exported_models/exported_best.pt", ) # The autolabeled masks will be saved in this format: # out/my_autolabeled_data # ├── .png # ├── .png # └── … ```
## 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/panoptic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/semantic_segmentation.html) | ✅ [🔗](https://docs.lightly.ai/train/stable/image_classification.html) | | EdgeCrafter | ✅ [🔗](https://docs.lightly.ai/train/stable/object_detection.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–YOLO26, 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. ## LightlyTrain in Research - [Unlabeled to Accurate: Self-Supervised Learning for Land Use Classification in Sentinel-2 Imagery](https://ieeexplore.ieee.org/abstract/document/11087068) - [Vision Foundry: A System for Training Foundational Vision AI Models](https://arxiv.org/abs/2512.11837) - [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739) - [Real-Time Object Detection Meets DINOv3](https://arxiv.org/abs/2509.20787) ## Usage Events LightlyTrain collects anonymous usage events to help us improve the product. 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