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