PyData Virginia 2025

Tutorial on Image Classification using Scikit-Image, Scikit-learn, and PyTorch
04-19, 09:00–10:30 (US/Eastern), Room 140

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Tutorial on building an image segmentation and classification pipeline for binary or multiclass classification using the popular packages scikit-learn, scikit-image and PyTorch.

Welcome to the exciting world of computer vision and machine learning! This tutorial presents foundational computer vision operations to prepare you to build your first successful classification pipeline. My goal is to help guide you past potential pitfalls and present topics for consideration as you embark on your machine learning journey.

  1. Computer Vision Basics
    * The Basics
    * Software and Packages
  2. Image Segmentation
    * Preprocessing (histograms, filters)
    * Thresholding
    * Morphological Operators
    * Advanced Segmentation
  3. Feature Extraction
    * Textures
    • GLCM
    • LBP
  4. Model Development - scikit-learn
    * scikit-learn
    • Gaussian Process
  5. Feature Importance
    * Shapley
  6. Neural Networks - PyTorch
  7. Model Development
    • CNN
    • Transfer Learning
  8. Model Performance
    * Tensorboard
    * Saliency map

Notebooks will be available prior to the start of the tutorial. Please come prepared with the following python packages installed:
* numpy
* pandas
* scikit-learn
* scikit-image
* torch
* torchvision
* pytensorboard

Prior Knowledge Expected –

No previous knowledge expected
See also:
Matt Litz

Matt Litz is a Data Science Engineer at BWX Technologies in Lynchburg, VA. He earned his Master's in Data Science from the University of Virginia in 2023. Primary research interests include computer vision and innovative approaches to implementing Large Language Models.