Semantic segmentation with Unet Deep Learning model applied to segment Cercospora Leaf Spot.
Created: - Updated:
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).
You can test and execute this module in various ways.
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
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.