lightly-ai / lightly

We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows to select the best core set of samples for model training through advanced filtering.

With lightly you can use 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.

Lightly is accessible also through a command-line interface (CLI). To train a SimCLR model on a folder of images you can simply run the following command:

Currently implemented models and their accuracy on cifar10. All models have been evaluated using kNN. We report the max test accuracy over the epochs as well as the maximum GPU memory consumption. All models in this benchmark use the same augmentations as well as the same ResNet-18 backbone. Training precision is set to FP32 and SGD is used as an optimizer with cosineLR. One epoch on cifar10 takes ~35 seconds on a V100 GPU. Learn more about the cifar10 benchmark here

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