Apple tree blossom image segmentation

We suggest a 2D image segmentation model based on UNET algorithm to segment images with blossoming apple tree

Model | Trainable | Inference | Pre-trained

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

Model Description

Build Status

Apple flowers are very sensitive to temperature The study of their development makes it possible to study climate change. This module proposes a model for the segmentation of apple blossom images. This model is based on the UNET algorithm. We suggest a tool for inference of images and a tool for retrain a model The model in inference and retraining is tensorflow model in h5 format.

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/uc-hereariim-deep-oc-blossom
$ docker run -ti -p 5000:5000 deephdc/uc-hereariim-deep-oc-blossom

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/uc-hereariim-deep-oc-blossom
(udocker) $ udocker create deephdc/uc-hereariim-deep-oc-blossom
(udocker) $ udocker run -p 5000:5000  deephdc/uc-hereariim-deep-oc-blossom

In either case, once the module is running, point your browser to 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.

Train this module

You can train this model using the DEEP framework. In order to execute this module in our pilot e-Infrastructure you would need to be registered in the DEEP IAM.

Once you are registedered, you can go to our training dashboard to configure and train it.

For more information, refer to the user documentation.


tensorflow, trainable, inference, pre-trained, deep learning, docker, api-v2


License: MIT

Configure and train

Get the code

Github Docker Hub