Lightly SSL is a computer vision framework for self-supervised learning.
For a commercial version with more features, including Docker support and pretraining models for embedding, classification, detection, and segmentation tasks with a single command, please contact sales@lightly.ai.
We've also built a whole platform on top, with additional features for active learning and data curation. If you're interested in the Lightly Worker Solution to easily process millions of samples and run powerful algorithms on your data, check out lightly.ai. It's free to get started!
This self-supervised learning framework offers the following features:
You can find sample code for all the supported models here. We provide PyTorch, PyTorch Lightning, and PyTorch Lightning distributed examples for all models to kickstart your project.
Models:
Want to jump to the tutorials and see Lightly in action?
Community and partner projects:
Lightly requires Python 3.7+. We recommend installing Lightly in a Linux or OSX environment. Python 3.12 is not yet supported, as PyTorch itself lacks Python 3.12 compatibility.
Due to the modular nature of the Lightly package some modules can be used with older versions of dependencies. However, to use all features as of today lightly requires the following dependencies:
Lightly is compatible with PyTorch and PyTorch Lightning v2.0+!
You can install Lightly and its dependencies from PyPI with:
pip3 install lightly
We strongly recommend that you install Lightly in a dedicated virtualenv, to avoid conflicting with your system packages.
With Lightly, you can use the latest self-supervised learning methods in a modular way using the full power of PyTorch. Experiment with different backbones, models, and loss functions. The framework has been designed to be easy to use from the ground up. Find more examples in our docs.
import torch
import torchvision
from lightly import loss
from lightly import transforms
from lightly.data import LightlyDataset
from lightly.models.modules import heads
# Create a PyTorch module for the SimCLR model.
class SimCLR(torch.nn.Module):
def __init__(self, backbone):
super().__init__()
self.backbone = backbone
self.projection_head = heads.SimCLRProjectionHead(
input_dim=512, # Resnet18 features have 512 dimensions.
hidden_dim=512,
output_dim=128,
)
def forward(self, x):
features = self.backbone(x).flatten(start_dim=1)
z = self.projection_head(features)
return z
# Use a resnet backbone.
backbone = torchvision.models.resnet18()
# Ignore the classification head as we only want the features.
backbone.fc = torch.nn.Identity()
# Build the SimCLR model.
model = SimCLR(backbone)
# Prepare transform that creates multiple random views for every image.
transform = transforms.SimCLRTransform(input_size=32, cj_prob=0.5)
# Create a dataset from your image folder.
dataset = LightlyDataset(input_dir="./my/cute/cats/dataset/", transform=transform)
# Build a PyTorch dataloader.
dataloader = torch.utils.data.DataLoader(
dataset, # Pass the dataset to the dataloader.
batch_size=128, # A large batch size helps with the learning.
shuffle=True, # Shuffling is important!
)
# Lightly exposes building blocks such as loss functions.
criterion = loss.NTXentLoss(temperature=0.5)
# Get a PyTorch optimizer.
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, weight_decay=1e-6)
# Train the model.
for epoch in range(10):
for (view0, view1), targets, filenames in dataloader:
z0 = model(view0)
z1 = model(view1)
loss = criterion(z0, z1)
loss.backward()
optimizer.step()
optimizer.zero_grad()
print(f"loss: {loss.item():.5f}")
You can easily use another model like SimSiam by swapping the model and the loss function.
# PyTorch module for the SimSiam model.
class SimSiam(torch.nn.Module):
def __init__(self, backbone):
super().__init__()
self.backbone = backbone
self.projection_head = heads.SimSiamProjectionHead(512, 512, 128)
self.prediction_head = heads.SimSiamPredictionHead(128, 64, 128)
def forward(self, x):
features = self.backbone(x).flatten(start_dim=1)
z = self.projection_head(features)
p = self.prediction_head(z)
z = z.detach()
return z, p
model = SimSiam(backbone)
# Use the SimSiam loss function.
criterion = loss.NegativeCosineSimilarity()
You can find a more complete example for SimSiam here.
Use PyTorch Lightning to train the model:
from pytorch_lightning import LightningModule, Trainer
class SimCLR(LightningModule):
def __init__(self):
super().__init__()
resnet = torchvision.models.resnet18()
resnet.fc = torch.nn.Identity()
self.backbone = resnet
self.projection_head = heads.SimCLRProjectionHead(512, 512, 128)
self.criterion = loss.NTXentLoss()
def forward(self, x):
features = self.backbone(x).flatten(start_dim=1)
z = self.projection_head(features)
return z
def training_step(self, batch, batch_index):
(view0, view1), _, _ = batch
z0 = self.forward(view0)
z1 = self.forward(view1)
loss = self.criterion(z0, z1)
return loss
def configure_optimizers(self):
optim = torch.optim.SGD(self.parameters(), lr=0.06)
return optim
model = SimCLR()
trainer = Trainer(max_epochs=10, devices=1, accelerator="gpu")
trainer.fit(model, dataloader)
See our docs for a full PyTorch Lightning example.
Or train the model on 4 GPUs:
# Use distributed version of loss functions.
criterion = loss.NTXentLoss(gather_distributed=True)
trainer = Trainer(
max_epochs=10,
devices=4,
accelerator="gpu",
strategy="ddp",
sync_batchnorm=True,
use_distributed_sampler=True, # or replace_sampler_ddp=True for PyTorch Lightning <2.0
)
trainer.fit(model, dataloader)
We provide multi-GPU training examples with distributed gather and synchronized BatchNorm. Have a look at our docs regarding distributed training.
Implemented models and their performance on various datasets. Hyperparameters are not tuned for maximum accuracy. For detailed results and more information about the benchmarks click here.
Note: Evaluation settings are based on these papers:
See the benchmarking scripts for details.
Model | Backbone | Batch Size | Epochs | Linear Top1 | Finetune Top1 | kNN Top1 | Tensorboard | Checkpoint |
---|---|---|---|---|---|---|---|---|
BarlowTwins | Res50 | 256 | 100 | 62.9 | 72.6 | 45.6 | link | link |
BYOL | Res50 | 256 | 100 | 62.5 | 74.5 | 46.0 | link | link |
DINO | Res50 | 128 | 100 | 68.2 | 72.5 | 49.9 | link | link |
MAE | ViT-B/16 | 256 | 100 | 46.0 | 81.3 | 11.2 | link | link |
MoCoV2 | Res50 | 256 | 100 | 61.5 | 74.3 | 41.8 | link | link |
SimCLR* | Res50 | 256 | 100 | 63.2 | 73.9 | 44.8 | link | link |
SimCLR* + DCL | Res50 | 256 | 100 | 65.1 | 73.5 | 49.6 | link | link |
SimCLR* + DCLW | Res50 | 256 | 100 | 64.5 | 73.2 | 48.5 | link | link |
SwAV | Res50 | 256 | 100 | 67.2 | 75.4 | 49.5 | link | link |
TiCo | Res50 | 256 | 100 | 49.7 | 72.7 | 26.6 | link | link |
VICReg | Res50 | 256 | 100 | 63.0 | 73.7 | 46.3 | link | link |
*We use square root learning rate scaling instead of linear scaling as it yields better results for smaller batch sizes. See Appendix B.1 in the SimCLR paper.
ImageNet100 benchmarks detailed results
Imagenette benchmarks detailed results
CIFAR-10 benchmarks detailed results
Below you can see a schematic overview of the different concepts in the package. The terms in bold are explained in more detail in our documentation.
Head to the documentation and see the things you can achieve with Lightly!
To install dev dependencies (for example to contribute to the framework) you can use the following command:
pip3 install -e ".[dev]"
For more information about how to contribute have a look here.
Unit tests are within the tests directory and we recommend running them using pytest. There are two test configurations available. By default, only a subset will be run:
make test-fast
To run all tests (including the slow ones) you can use the following command:
make test
To test a specific file or directory use:
pytest <path to file or directory>
To format code with black and isort run:
make format
Self-Supervised Learning:
Why should I care about self-supervised learning? Aren't pre-trained models from ImageNet much better for transfer learning?
How can I contribute?
Is this framework for free?
If this framework is free, how is the company behind Lightly making money?
Lightly is a spin-off from ETH Zurich that helps companies build efficient active learning pipelines to select the most relevant data for their models.
You can find out more about the company and it's services by following the links below:
If you want to cite the framework feel free to use this:
@article{susmelj2020lightly,
title={Lightly},
author={Igor Susmelj and Matthias Heller and Philipp Wirth and Jeremy Prescott and Malte Ebner et al.},
journal={GitHub. Note: https://github.com/lightly-ai/lightly},
year={2020}
}