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ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy

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ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy

Tl;dr: this wiki page has everything you need to get started.


What is this?

ZeroCostDL4Mic is a collection of self-explanatory Jupyter Notebooks for Google Colab that features an easy-to-use graphical user interface. They are meant to quickly get you started on learning to use deep-learning for microscopy. Google Colab itself provides the computations resources needed at no-cost. ZeroCostDL4Mic is designed for researchers that have little or no coding expertise to quickly test, train and use popular Deep-Learning networks.

Want to see a short video demonstration?

| Running a ZeroCostDL4Mic notebook | Example data in ZeroCostDL4Mic | Romain's talk @ Aurox conference | Talk @ SPAOM | |:-:|:-:|:-:|:-:| | | | | |

Who is it for?

Any researcher interested in microscopy, independent of their background training. ZeroCostDL4Mic is designed for anyone with little or no coding expertise to quickly test, train and use popular Deep-Learning networks used to process microscopy data.


This project initiated as a collaboration between the Jacquemet and Henriques laboratories, considerably expanding with the help of laboratories spread across the planet. There is a long list of contributors associated with the project acknowledged in our related paper and the wiki page.

How to cite this work

Lucas von Chamier, Romain F. Laine, Johanna Jukkala, Christoph Spahn, Daniel Krentzel, Elias Nehme, Martina Lerche, Sara Hernández-pérez, Pieta Mattila, Eleni Karinou, Séamus Holden, Ahmet Can Solak, Alexander Krull, Tim-Oliver Buchholz, Martin L Jones, Loic Alain Royer, Christophe Leterrier, Yoav Shechtman, Florian Jug, Mike Heilemann, Guillaume Jacquemet, Ricardo Henriques. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications, 2021. DOI:

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