Paper accepted

Our publication, A comparison of machine learning methods for cross-domain few-shot learning, has been accepted in the 33rd Australasian Joint Conference on Artificial Intelligence, Canberra, Australia.

For more details and downloads, see our publications page.

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:

DeepSpeech Docker image available

A new Docker image is available for training DeepSpeech models using a GPU backend. The image is based on Mozilla's DeepSpeech 0.7.4 one for training models, adding more functionality to it:

  • support for MP3 and OGG files, not just WAV

  • automatic alphabet generation from the transcripts

  • split sound files into chunks based on detected pauses

  • batch process audio files (can be continuous)

  • many Python utilities have been sym-linked

More information on the Docker image is available from Github:

github.com/waikato-datamining/tensorflow/tree/master/deepspeech

wai.annotations release 0.4.0

A new release of wai.annotations is out now: 0.4.0

This release represents a major refactoring, introducing domains other than object-detection in images. Another notable change is being able to add filters into the conversion pipeline. This will, e.g., allow the augmentation of datasets while converting them. In terms of object detection datasets, these augmentations can be rotation, cropping, brightening/darkening, etc. Though a lot of frameworks already support basic augmentation transformations, trying to automatically augment bounding boxes rather than object shapes can lead to strange and undesirable artifacts (e.g., overly large boxes, overlapping annotations).