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LightlyTrain documentation
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0.13.0 ▼
  • Quick Start - Object Detection
  • Quick Start - Distillation
  • Installation
  • Object Detection
  • Instance Segmentation
  • Panoptic Segmentation
  • Semantic Segmentation
  • Pretrain & Distill
    • Overview
    • Distillation
    • Pretrain DINOv2
    • All Methods
      • Overview
      • Distillation
      • DINOv2
      • DINO
      • SimCLR
    • Models
      • Overview
      • Torchvision
      • TIMM
      • Ultralytics
      • RT-DETR
      • RF-DETR
      • YOLOv12
      • SuperGradients
      • Custom Models
    • Configuring Image Transforms
    • Export
  • Predict & Autolabel
  • Embed
  • Data Input
    • Single- and Multi-Channel Images
    • DICOM Images
  • Performance
    • Multi-GPU
    • Multi-Node
    • Hardware Recommendations
  • Docker
  • Tutorials
    • Classification with Torchvision’s ResNet
    • Object Detection with Ultralytics’ YOLO
    • Monocular Depth Estimation with fastai U-Net (Advanced)
    • Embedding Model for Satellite Images with Torchvision’s ResNet
    • Google Colab Notebooks
  • Python API
    • lightly_train
  • FAQ
  • Changelog
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Tutorials¶

In the following, some tutorials using LightlyTrain can be found. The tutorials are a good starting point to learn about LightlyTrain.

  • Classification with Torchvision’s ResNet
  • Object Detection with Ultralytics’ YOLO
  • Monocular Depth Estimation with fastai U-Net (Advanced)
  • Embedding Model for Satellite Images with Torchvision’s ResNet

We’ve also prepared some Google Colab notebooks for some packages we support. You can find them in the following page:

  • Google Colab Notebooks
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Classification with Torchvision’s ResNet
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