2D semantic segmentation multilabels with Unet

Semantic segmentation with Unet Deep Learning model applied to segment Cercospora Leaf Spot.


Model

Published by DEEP-Hybrid-DataCloud Consortium
Created: - Updated:

Model Description

Build Status

This is 2D semantic segmentation multilabels with application using DEEPaaS API. The dataset used to train this model contains three classes: Background, Leaf and Disease (Cercospora).

Test this module

You can test and execute this module in various ways.

Excecute locally on your computer using Docker

You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:

$ docker pull deephdc/deep-oc-unet
$ docker run -ti -p 5000:5000 deephdc/deep-oc-unet

Execute on your computer using udocker

If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:

$ virtualenv udocker
$ source udocker/bin/activate
(udocker) $ pip install udocker
(udocker) $ udocker pull deephdc/deep-oc-unet
(udocker) $ udocker create deephdc/deep-oc-unet
(udocker) $ udocker run -p 5000:5000  deephdc/deep-oc-unet

In either case, once the module is running, point your browser to http://127.0.0.1:5000/ and you will see the API documentation, where you can test the module functionality, as well as perform other actions (such as training).

For more information, refer to the user documentation.

Categories

docker, api-v2

License

License: MIT

Get the code

Github Docker Hub