Skip to content

haberlmatt/cdeep3m-colab

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

CDeep3M2-Colab

CDeep3M2 installation and interface on Google Colab

CDeep3M Preview:

Please note: The easiest way to run CDeep3M (for free!) is using the CDeep3M preview function: CDeep3M-Preview at the cellimagelibrary.org The links below provide quick entry points with graphical user interface (GUI) or command line interface (CLI) to run CDeep3M on your own using Googles free GPUs. Due to limitations of runtime duration (12h) this can be enough for some tasks, like small segmentation tasks or re-training a pre-trained network


Prediction GUI:

Run CDeep3M predictions with a graphical user interface (GUI) on Google Colab's free GPUs.


Re-training GUI:

Apply transfer learning of a CDeep3M pre-trained model on GUI.


CDeep3M-Colab CLI:

If you are comfortable using a command line interface, this provides the most flexible way to use all functionality of CDeep3M while using Googles free GPUs. It performs the complete CDeep3M installation and sets you up with the CDeep3M CLI on colab.


Training-GUI:

This provides a graphical user interface (GUI) to run a CDeep3M training on your own using Googles free GPUs. Disclaimer: Due to the limited runtime and potential interuption during the execution, training a network from scratch on Colab is not recommended. please either consider re-training a network from the modelzoo or starting a training from scratch using a Docker container and accessible GPUs in this case.


About

CDeep3M installation and graphical user interface on Colab

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published