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Naïve Bees: Deep Learning with Images

Build a deep learning model that can automatically detect honey bees and bumble bees in images.

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12 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 build a simple deep learning model that can automatically detect honey bees and bumble bees, then load a pre-trained model for evaluation. You will use keras, scikit-learn, scikit-image, and numpy, among other popular Python libraries.

This project is the third 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.

Before taking this project, it will help to have completed Naïve Bees: Image Loading and Processing and Naïve Bees: Predict Species from Images.

Project Tasks

  1. 1
    Import Python libraries
  2. 2
    Load image labels
  3. 3
    Examine RGB values in an image matrix
  4. 4
    Importing the image data
  5. 5
    Split into train, test, and evaluation sets
  6. 6
    Normalize image data
  7. 7
    Model building (part i)
  8. 8
    Model building (part ii)
  9. 9
    Compile and train model
  10. 10
    Load pre-trained model and score
  11. 11
    Visualize model training history
  12. 12
    Generate predictions


Python Python


Data ManipulationData VisualizationMachine Learning
Emily Miller HeadshotEmily 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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