Package: root-painter-trainer
RootPainterRootPainter is a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis.
RootPainter uses a client-server architecture, allowing users with a typical laptop to utilise a GPU on a more computationally powerful server.A detailed description is available in the paper published in the New PhytologistRootPainter: Deep Learning Segmentation of Biological Images with Corrective AnnotationTo see a list of work using (or citing) the RootPainter paper, please see thegoogle scholar pageA BioRxiv Pre-print (earlier version of the paper) is available at:https://www.biorxiv.org/content/10.1101/2020.04.16.044461v2Getting started quicklyI suggest thecolab tutorial.A  shortermini guideis available including more concise instruction, that could be used as reference. I suggest the paper, videos and then colab tutorial to get an idea of how the software interface could be used and then this mini guide for reference to help remember each of the key steps to get from raw data to final measurements.VideosA video demonstrating how to train and use a model is available todownloadThere is ayoutube videoof a workshop explaining the background behind the software and covering using the colab notebook to train and use a root segmentation model.Client DownloadsSeereleasesIf you are not confident installing and running python applications on the command line then to get started quickly I suggest thecolab tutorial.Server setupThe following instructions are for a local server. If you do not have a suitable NVIDIA GPU with at least 8GB of GPU memory then my current recommendation is to run via Google colab. A publicly available notebook is available atGoogle Drive with Google Colab.Other options to run the server component of RootPainter on a remote machine include thethe sshfs server setup tutorial. You can also use Dropbox instead of sshfs.For the next steps I assume you have a suitable GPU and CUDA installed.To install the RootPainter trainer:pip install root-painter-trainerTo run the trainer.  This will first create the sync directory.start-trainerYou will be prompted to input a location for the sync directory. This is the folder where files are shared between the client and server. I will use ~/root_painter_sync.
RootPainter will then create some folders inside ~/root_painter_sync.
The server should print the automatically selected batch size, which should be greater than 0. It will then start watching for instructions from the client.You should now be able to see the folders created by RootPainter (datasets, instructions and projects) inside ~/Desktop/root_painter_sync on your local machine
Seelung tutorialfor an example of how to use RootPainter to train a model. I now actually suggest following thecolab tutorialinstructions but using your local setup instead of the colab server, as these are easier to follow than the lung tutorial.Questions and ProblemsTheFAQmay  be worth checking before reaching out with any questions you have. If you do have a question you can either email me or post in thediscussions. If you have an issue/ have identified a problem with the software then you canpost an issue.