Tutorials

Posted on mar. 19 d├ęcembre 2017 in tutorial by Kazuhiro Terao

If you are looking for "how to get started" with hands-on experience, you are in the right place. Below is a summary of tutorials made available voluntarily by our group members. The most (if not all) of them are written in python, and in fact in jupyter notebook format.

Installing Jupyter Notebook

Jupyter notebook is not a requirement for using our software, but we share lots of examples in this format. So you might want to install it if you don't have one. Installation is same as many other python packages.

  1. If you don't have it, install either pip or conda python package manager.
  2. Either pip install or conda install jupyter.

You might find a few glitches. Try googling a solution by copy-and-paste the error message (usually "package X not found") in the browser.

Additional packages: other extremely useful python packages include numpy, scipy, matplotlib, scikit-learn, and scikit-image. Optional packages: want more? you can install tensorflow and pyqtgraph!

Quick Start

Before starting, git clone our tutorial repository. All notebooks in this section can be found there (and you can execute/run the notebook by yourself as you follow the tutorial).

Introduction to Open Data

These links are the blog posts that covered the contents of data files made available in our public data.

  • Image classification sample
    • Images of 5 different particle types, prepared for an image classification challenge.
  • Semantic-segmentation sample
    • Images of many particles prepared for semantic-segmentation challenge. Can be also used for object detection and instance-aware semantic segmentation algorithm training!

Training on Open Data

These are tutorial notebooks that actually train a toy algorithm for available open data. Like Quick Start, you can find these notebooks in our tutorial repository. But these examples use our public data which you have to download before executing the notebooks.

LArCV

Quick Start serves as an introduction to LArCV. You can checkout the repository's wiki for more through guidance. Below you find some notebooks that are referred to in the wiki (but you can also just browse through here, if you want to!).