Self-supervised learning

Lightly is a computer vision framework for training deep learning models using self-supervised learning. The framework can be used for a wide range of useful applications such as finding the nearest neighbors, similarity search, transfer learning, or data analytics.

Additionally, you can use the Lightly framework to directly interact with the lightly platform. Check out our section on lightly-platform for more information.

How Lightly Works

The flexible design of Lightly makes it easy to integrate in your Python code. Lightly is built completely around PyTorch frameworks and the different pieces can be put together to fit your requirements.

Data and Transformations

The basic building block of self-supervised methods such as SimCLR are image transformations. Each image is transformed into two new images by randomly applied augmentations. The task of the self-supervised model is then to identify the images which come from the same original among a set of negative examples.

Lightly implements these transformations as torchvision transforms in the collate function of the dataloader. For example, the collate function below will apply two different, randomized transforms to each image: A randomized resized crop and a random color jitter.

import as data

# the collate function applies random transforms to the input images
collate_fn = data.ImageCollateFunction(input_size=32, cj_prob=0.5)

Let’s now load an image dataset and create a PyTorch dataloader with the collate function from above.

import torch

# create a dataset from your image folder
dataset = data.LightlyDataset(input_dir='./my/cute/cats/dataset/')

# build a PyTorch dataloader
dataloader =
    dataset,                # pass the dataset to the dataloader
    batch_size=128,         # a large batch size helps with the learning
    shuffle=True,           # shuffling is important!
    collate_fn=collate_fn)  # apply transformations to the input images


You can also use a custom PyTorch Dataset instead of the LightlyDataset. Just make sure your Dataset implementation returns a tuple of (sample, target, filename) to support the basic functions for training models. See for more information.

Head to the next section to see how you can train a ResNet on the data you just prepared.

Model, Loss and Training

Now, we need an embedding model, an optimizer and a loss function. We use a ResNet together with the normalized temperature-scaled cross entropy loss and simple stochastic gradient descent.

import torchvision

from lightly.loss import NTXentLoss
from lightly.models.modules.heads import SimCLRProjectionHead

# use a resnet backbone
resnet = torchvision.models.resnet18()
resnet = torch.nn.Sequential(*list(resnet.children())[:-1])

# build a SimCLR model
class SimCLR(torch.nn.Module):
    def __init__(self, backbone, hidden_dim, out_dim):
        self.backbone = backbone
        self.projection_head = SimCLRProjectionHead(hidden_dim, hidden_dim, out_dim)

    def forward(self, x):
        h = self.backbone(x).flatten(start_dim=1)
        z = self.projection_head(h)
        return z

model = SimCLR(resnet, hidden_dim=512, out_dim=128)

# use a criterion for self-supervised learning
# (normalized temperature-scaled cross entropy loss)
criterion = NTXentLoss(temperature=0.5)

# get a PyTorch optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=1e-0, weight_decay=1e-5)


You can also use custom backbones and use lightly to train them using self-supervised learning. Learn more about how to use custom backbones in our colab playground.

Train the model for 10 epochs.

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
max_epochs = 10
for epoch in range(max_epochs):
    for (x0, x1), _, _ in dataloader:

        x0 =
        x1 =

        z0 = model(x0)
        z1 = model(x1)

        loss = criterion(z0, z1)


Congrats, you just trained your first model using self-supervised learning!

You can of course also use PyTorch Lightning to implement and train your model.

import pytorch_lightning as pl

class SimCLR(pl.LightningModule):
    def __init__(self, backbone, hidden_dim, out_dim):
        self.backbone = backbone
        self.projection_head = SimCLRProjectionHead(hidden_dim, hidden_dim, out_dim)
        self.criterion = NTXentLoss(temperature=0.5)

    def forward(self, x):
        h = self.backbone(x).flatten(start_dim=1)
        z = self.projection_head(h)
        return z

    def training_step(self, batch, batch_idx):
        (x0, x1), _, _ = batch
        z0 = self.forward(x0)
        z1 = self.forward(x1)
        loss = self.criterion(z0, z1)
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.parameters(), lr=1e-0)
        return optimizer

model = SimCLR(resnet, hidden_dim=512, out_dim=128)
trainer = pl.Trainer(max_epochs=max_epochs, devices=1, accelerator="gpu")

To train on a machine with multiple GPUs we recommend using the distributed data parallel strategy.

# If we have a machine with 4 GPUs we set devices=4 and accelerator="gpu".
trainer = pl.Trainer(


You can use the trained model to embed your images or even access the embedding model directly.

# make a new dataloader without the transformations
# The only transformation needed is to make a torch tensor out of the PIL image
dataset.transform = torchvision.transforms.ToTensor()
dataloader =
    dataset,        # use the same dataset as before
    batch_size=1,   # we can use batch size 1 for inference
    shuffle=False,  # don't shuffle your data during inference

# embed your image dataset
embeddings = []
with torch.no_grad():
    for img, label, fnames in dataloader:
        img =
        emb = model.backbone(img).flatten(start_dim=1)

    embeddings =, 0)

Done! You can continue to use the embeddings to find nearest neighbors or do similarity search. Furthermore, the ResNet backbone can be used for transfer and few-shot learning.

# access the ResNet backbone
resnet = model.backbone


Self-supervised learning does not require labels for a model to be trained on. Lightly, however, supports the use of additional labels. For example, if you train a model on a folder ‘cats’ with subfolders ‘Maine Coon’, ‘Bengal’ and ‘British Shorthair’ Lightly automatically returns the enumerated labels as a list.

Lightly in Three Lines

Lightly also offers an easy-to-use interface. The following lines show how the package can be used to train a model with self-supervision and create embeddings with only three lines of code.

from lightly.core import train_embedding_model, embed_images

# first we train our model for 10 epochs
checkpoint = train_embedding_model(input_dir='./my/cute/cats/dataset/', trainer={'max_epochs': 10})

# let's embed our 'cats' dataset using our trained model
embeddings, labels, filenames = embed_images(input_dir='./my/cute/cats/dataset/', checkpoint=checkpoint)

# now, let's inspect the shape of our embeddings