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Naïve Bees: Image Loading and Processing

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

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10 Tasks1,500 XP

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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.

Project Tasks

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

Technologies

Python Python

Topics

Data ManipulationData VisualizationAI & Machine LearningImporting & Cleaning Data
Peter Bull HeadshotPeter 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 HeadshotEmily Miller

Data Scientist at DrivenData

Emily Miller is a Data Scientist at DrivenData. Her personal passion is to combine data science with satellite imagery, text data, and other non-traditional data sources to make poverty alleviation efforts more effective. She was previously a Data Scientist at the Bill & Melinda Gates Foundation and a Data Science Fellow at Metis.
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