Quick Start

Installation

pip install lightly-train

Important

Check the Installation page for supported platforms.

Prepare Data

You can use any image dataset for training. No labels are required, and the dataset can be structured in any way, including subdirectories. If you don’t have a dataset at hand, you can download one like this:

git clone https://github.com/lightly-ai/dataset_clothing_images.git my_data_dir
rm -rf my_data_dir/.git

See the data guide for more information on supported data formats.

Train

Once the data is ready, you can train the model like this:

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",       # Model to train
        epochs=10,                          # Number of epochs to train
        batch_size=32,                      # Batch size
    )

Command Line

lightly-train train out="out/my_experiment" data="my_data_dir" model="torchvision/resnet18" epochs=10 batch_size=32

Important

This is a minimal example for illustration purposes. In practice you would want to use a larger dataset (>=10’000 images), more epochs (>=100), and a larger batch size (>=128).

Best Choice: The default pretraining method distillation is recommended, as it consistently outperforms others in extensive experiments. Batch sizes between 128 and 1536 strike a good balance between speed and performance. Moreover, long training runs, such as 2,000 epochs on COCO, significantly improve results.

Tip

LightlyTrain supports many popular models out of the box.

This pretrains a Torchvision ResNet-18 model using images from my_data_dir. All training logs, model exports, and checkpoints are saved to the output directory at out/my_experiment.

Once the training is complete, the out/my_experiment directory contains the following files:

out/my_experiment
├── checkpoints
│   ├── epoch=99-step=123.ckpt          # Intermediate checkpoint
│   └── last.ckpt                       # Last checkpoint
├── events.out.tfevents.123.0           # Tensorboard logs
├── exported_models
|   └── exported_last.pt                # Final model exported
├── metrics.jsonl                       # Training metrics
└── train.log                           # Training logs

The final model is exported to out/my_experiment/exported_models/exported_last.pt in the default format of the used library. It can directly be used for fine-tuning. See export format for more information on how to export models to other formats or on how to export intermediate checkpoints.

While the trained model has already learned good representations of the images, it cannot yet make any predictions for tasks such as classification, detection, or segmentation. To solve these tasks, the model needs to be fine-tuned on a labeled dataset.

Fine-Tune

Now the model is ready for fine-tuning! You can use your favorite library for this step. Below is a simple example using PyTorch:

import torch
import torchvision.transforms.v2 as v2
import tqdm
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets, models

transform = v2.Compose([
    v2.Resize((224, 224)),
    v2.ToImage(),
    v2.ToDtype(torch.float32, scale=True),
])
dataset = datasets.ImageFolder(root="my_data_dir", transform=transform)
dataloader = DataLoader(dataset, batch_size=16, shuffle=True, drop_last=True)

# Load the exported model
model = models.resnet18()
model.load_state_dict(torch.load("out/my_experiment/exported_models/exported_last.pt", weights_only=True))

# Update the classification head with the correct number of classes
model.fc = nn.Linear(model.fc.in_features, len(dataset.classes))

device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
model.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
print("Starting fine-tuning...")
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    progress_bar = tqdm.tqdm(dataloader, desc=f"Epoch {epoch+1}/{num_epochs}")
    for inputs, labels in progress_bar:
        optimizer.zero_grad()
        outputs = model(inputs.to(device))
        loss = criterion(outputs, labels.to(device))
        loss.backward()
        optimizer.step()
        progress_bar.set_postfix(loss=f"{loss.item():.4f}")
    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")

The output shows the loss decreasing over time:

Starting fine-tuning...
Epoch [1/10], Loss: 2.1686
Epoch [2/10], Loss: 2.1290
Epoch [3/10], Loss: 2.1854
Epoch [4/10], Loss: 2.2936
Epoch [5/10], Loss: 1.9303
Epoch [6/10], Loss: 1.9949
Epoch [7/10], Loss: 1.8429
Epoch [8/10], Loss: 1.9873
Epoch [9/10], Loss: 1.8179
Epoch [10/10], Loss: 1.5360

Congratulations! You just trained and fine-tuned a model using LightlyTrain!

Embed

Instead of fine-tuning the model, you can also use it to generate image embeddings. This is useful for clustering, retrieval, or visualization tasks. The embed command generates embeddings for all images in a directory:

Python

import lightly_train

if __name__ == "__main__":
    lightly_train.embed(
        out="my_embeddings.pth",                                # Exported embeddings
        checkpoint="out/my_experiment/checkpoints/last.ckpt",   # LightlyTrain checkpoint
        data="my_data_dir",                                     # Directory with images
    )

Command Line

lightly-train embed out="my_embeddings.pth" checkpoint="out/my_experiment/checkpoints/last.ckpt" data="my_data_dir"

The embeddings are saved to my_embeddings.pth and are loaded like this:

import torch

embeddings = torch.load('my_embeddings.pth')
print(embeddings['filenames'][:5])       # Print first five filenames
print(embeddings['embeddings'].shape)    # Tensor with embeddings with shape (num_images, embedding_dim)