Metadata-Version: 2.1
Name: root-painter-trainer
Version: 0.2.25.3
Summary: Trainer (server component) for RootPainter
Home-page: https://github.com/Abe404/root_painter
Author: Abraham George Smith
License: GPL-2.0
Description: ## RootPainter
        
        Described in the paper "RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation"
        
        Published peer-reviewed paper available in the New Phytologist at:
        [https://doi.org/10.1111/nph.18387](https://doi.org/10.1111/nph.18387)
        
        To see a list of work using (or citing) the RootPainter paper, please see the [google scholar page](https://scholar.google.com/scholar?cites=12740268016453642124)
        
        BioRxiv Pre-print available at:
        [https://www.biorxiv.org/content/10.1101/2020.04.16.044461v2](https://www.biorxiv.org/content/10.1101/2020.04.16.044461v2)
        
        RootPainter 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.   
        
        ### Getting started quickly
        
         I suggest the [colab tutorial](https://colab.research.google.com/drive/104narYAvTBt-X4QEDrBSOZm_DRaAKHtA?usp=sharing).
         
         A  shorter [mini guide](https://github.com/Abe404/root_painter/blob/master/docs/mini_guide.md) is 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.
         
        ### Videos
        A video demonstrating how to train and use a model is available to [download](https://nph.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fnph.18387&file=nph18387-sup-0002-VideoS1.mp4)
        
        There is a [youtube video](https://www.youtube.com/watch?v=73u73tBvRO4) of a workshop explaining the background behind the software and covering using the colab notebook to train and use a root segmentation model.
        
        
        ### Client Downloads
        
        See [releases](https://github.com/Abe404/root_painter/releases) 
        
        If you are not confident installing and running python applications on the command line then to get started quickly I suggest the [colab tutorial](https://colab.research.google.com/drive/104narYAvTBt-X4QEDrBSOZm_DRaAKHtA?usp=sharing).
        
        #### Server setup 
        
        The 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 at [Google Drive with Google Colab](https://colab.research.google.com/drive/104narYAvTBt-X4QEDrBSOZm_DRaAKHtA?usp=sharing).
        
        Other options to run the server component of RootPainter on a remote machine include the [the sshfs server setup tutorial](https://github.com/Abe404/root_painter/blob/master/docs/server_setup_sshfs.md). You can also use Dropbox instead of sshfs.
        
        
        For the next steps I assume you have a suitable GPU and CUDA installed.
        
        1. To install the RootPainter trainer:
        
        ```
        pip install root-painter-trainer
        ```
        
        2. To run the trainer.  This will first create the sync directory.
        
        ```
        start-trainer
        ```
        
        You 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 
        See [lung tutorial](docs/cxr_lung_tutorial.md) for an example of how to use RootPainter to train a model.
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
