Keras image segmentation Docker image available

A new Docker image is available for training Keras image segmentation models using a GPU backend. The image is based on TensorFlow 1.14 and Divam Gupta's code, plus additional tools for converting indexed PNGs into RGB ones and continuously processing images with a model.

More information on the Docker image is available from Github:

github.com/waikato-datamining/tensorflow/tree/master/image-segmentation-keras

wai.lazypip release

An initial release of wai.lazypip is out now: 0.0.1

wai.lazypip is a little helper library that can install additional packages within a virtual environment on demand if required modules, functions, attributes or classes are not present. Under the hood, pip is used for installing the additional packages.

ADAMS snapshots now publicly available

The newly available ufdl-frontend-adams modules for ADAMS now have public builds available for download:

adams.cms.waikato.ac.nz/snapshots/ufdl/

As of now, the following workflows can be used for managing a UFDL server instance and datasets:

  • adams-ufdl-core-manage_backend.flow - manages users, teams, projects, licenses

  • adams-ufdl-image-manage_image_classification_datasets.flow - for image classifications datasets

  • adams-ufdl-image-manage_object_detection_datasets.flow - for object detection datasets

  • adams-ufdl-speech-manage_speech_datasets.flow - for speech datasets

NB: In order to utilize these flows, you need to have an instance of the ufdl-backend running, of course.

Github repositories now publicly availably

The (very much work-in-progress) code of the following UFDL repositories is now publicly available: