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Qu is an attempt to make the full deep learning workflow more interactive by providing a user interface (implemented as napari widget) that abstracts all steps from ground truth generation and curation to training and prediction.

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Qu

Qu is an attempt to make the full deep learning workflow more interactive by providing a user interface that abstracts all steps from ground truth generation and curation to training and prediction.

Qu is implemented as a plug-in for the great napari multi-dimensional image viewer for python and makes heavy use of the MONAI framework.

Qu

Qu is released under the terms of the Apache License version 2.0 (see LICENSE). All libraries used by Qu have their own licenses.

Installation

Create an environment and install napari

Install napari as explained in the official documentation. It is recommended to create a dedicated environment:

conda create -y -n napari-env python=3.9
conda activate napari-env
pip install "napari[all]"

The next steps assume that we activated the napari-env environment.

Install PyTorch

It is recommented to install PyTorch using the selection tool on https://pytorch.org/get-started/locally/#start-locally. This will ensure that PyTorch is installed with GPU acceleration and with the correct version of the CUDA libraries.

Example: conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

Adapt the example as needed.

Install Qu

Qu cannot be installed from the napari-hub yet. Instead, clone Qu and install it manually as a napari plug in as follows:

git clone https://github.com/aarpon/qu
cd qu
pip install -e .

Note: Qu still uses the first generation napari-plugin-engine: a migration to npe2 is planned.

Getting started

Qu can be started from the Plugins menu. The Qu main menu can be opened right-clicking on the Qu main widget.

From the Demos menu, choose Segmentation dataset: 2|3 classes or Restoration dataset.

Note: Qu cannot be installed from the napari-hub yet.

User manual

Detailed instructions will follow soon.

About

Qu is an attempt to make the full deep learning workflow more interactive by providing a user interface (implemented as napari widget) that abstracts all steps from ground truth generation and curation to training and prediction.

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