Super-Selfish: Self-Supervised Learning on Images with PyTorch
This framework simplifies the application of self-supervised learning for researchers and practitioners working with image data, making advanced techniques more accessible.
This paper introduces Super-Selfish, a PyTorch framework for image-based self-supervised learning. It provides 13 algorithms, ranging from simple classification to state-of-the-art contrastive tasks, enabling pretraining of any PyTorch neural network with two lines of code.
Super-Selfish is an easy to use PyTorch framework for image-based self-supervised learning. Features can be learned with 13 algorithms that span from simple classification to more complex state of theart contrastive pretext tasks. The framework is easy to use and allows for pretraining any PyTorch neural network with only two lines of code. Simultaneously, full flexibility is maintained through modular design choices. The code can be found at https://github.com/MECLabTUDA/Super_Selfish and installed using pip install super-selfish.