Torchvision

This page describes how to use Torchvision models with LightlyTrain.

Pretrain a Torchvision Model

Pretraining Torchvision models with LightlyTrain is straightforward. Below we will provide the minimum scripts for pretraining using torchvision/resnet18 as an example:

Python

import lightly_train

if __name__ == "__main__":
    lightly_train.train(
        out="out/my_experiment",                # Output directory.
        data="my_data_dir",                     # Directory with images.
        model="torchvision/resnet18",           # Pass the Torchvision model.
    )

Command Line

lightly-train train out="out/my_experiment" data="my_data_dir" model="torchvision/resnet18"

You can reload the exported model by:

import torch
from torchvision.models import resnet18

model = resnet18()
state_dict = torch.load("out/my_experiment/exported_models/exported_last.pt")
model.load_state_dict(state_dict)

Supported Models

The following Torchvision models are supported:

• ResNet

  • torchvision/resnet18

  • torchvision/resnet34

  • torchvision/resnet50

  • torchvision/resnet101

  • torchvision/resnet152

• ConvNext

  • torchvision/convnext_base

  • torchvision/convnext_large

  • torchvision/convnext_small

  • torchvision/convnext_tiny