Eisen is an open source package that facilitates training, validation, testing and deployment of deep learning algorithms. Check out the documentation now!
Eisen proposes simple interfaces to complex functionality. No over-engineering means developers can achieve more with less effort.
Eisen is built to be modular, so you can use only what you need! It is mostly compatible with other packages in the PyTorch universe.
... to test Eisen on a powerful GPU directly in your browser!
Eisen is and will always be free and open source. We have built it to serve the needs of a community of developers who need a solid foundation to do science. We aim to make development simple, experiments reproducible and deployment of DL models straightforward. Our philosophy is to avoid over-engineering and keep a simple, clean and readable code base. Feel free to contribute and improve the code, or just create your own version of it.
Eisen offers an opinionanted API to tap into medical image analysis and volumetric image understanding capabilties. Our architecture is inspired by Torchvision and is mostly compatible with other software packages in the PyTorch universe. You can mix and match Eisen modules and take the functionality you need into your projects. You can access Eisen by importing it as you would do with any other python packages or by taking advantage of its command line interface (CLI). Find out more here.
From training to deployment, Eisen is always with you. In addition to python coding, you can build your experiments visually via a convenient and simple user interface allowing users to configure every aspect of training, validation, testing and deployment in just a few clicks. Once your models are ready, you can make them available in a server-client fashion no matter the number and kind of their input and outputs. Find out more about this and other features here!
Eisen is free and open source. Our software is organized in several sub-packages that you can fork and contribute to on GitHub. Reach out on Slack.
Run in Google Colab
Fork me on GitHub!