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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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  • 11 tasks
  • 2,596 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 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.

The recommended prerequisites for this project are Advanced Deep Learning with Keras in Python, Introduction to Data Visualization with Python, Naïve Bees: Image Loading and Processing, and Naïve Bees: Predict Species from Images.

Project Tasks

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

    Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data