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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 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](https://keras.io/), [scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html), [scikit-image](http://scikit-image.org/docs/stable/), and [numpy](https://docs.scipy.org/doc/numpy-1.14.2/reference/), 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](https://www.datacamp.com/projects/374) and [Naïve Bees: Predict Species from Images](https://www.datacamp.com/projects/412).

Project Tasks

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

Technologies

Python Python

Topics

Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data
Emily Miller Headshot

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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