Premium Project

Naïve Bees: Image Loading and Processing

Load, transform, and understand images of honey bees and bumble bees in Python.

  • 10 tasks
  • 2,499 participants
  • 1,500 XP

Project Description

Can a machine distinguish between a honey bee and a bumble bee? Being able to identify bee species from images, while challenging, would allow researchers to more quickly and effectively collect field data. In this Project, you will use the Python image library Pillow to load and manipulate image data. You'll learn common transformations of images and how to build them into a pipeline.

This project is the first part of a series of projects that walk through working with image data, building classifiers using traditional techniques, and leveraging the power of deep learning for computer vision. The second project in the series is Naïve Bees: Predict Species from Images.

The recommended prerequisites for this project are Intermediate Python for Data Science and Introduction to Data Visualization with Python.

Project Tasks

  • 1 Import Python libraries
  • 2 Opening images with PIL
  • 3 Image manipulation with PIL
  • 4 Images as arrays of data
  • 5 Explore the color channels
  • 6 Honey bees and bumble bees (i)
  • 7 Honey bees and bumble bees (ii)
  • 8 Simplify, simplify, simplify
  • 9 Save your work!
  • 10 Make a pipeline
Peter Bull
Peter Bull

Co-founder of DrivenData

Peter is a co-founder of DrivenData. He earned his master's in Computational Science and Engineering from Harvard’s School of Engineering and Applied Sciences. His work lies at the intersection of statistics and computer science, and he wants to help bring powerful new modeling techniques to the organizations that need them most. He previously worked as a software engineer at Microsoft and earned a BA in philosophy from Yale University.

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Emily Miller
Emily Miller

Data Scientist at DrivenData

Emily is a data scientist at DrivenData. With a background in international development, her interests lie in using data science to make poverty alleviation efforts more effective. She previously worked at the Bill & Melinda Gates Foundation, Stanford Center for International Development, and Brookings Institution. She holds a master's in International Development from The New School.

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  • Python
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