Dog breed identification app

This repository contains a dog breed identification app that I built as my capstone project for the Data Scientist nanodegree at Udacity.

See the accompanying Jupyter notebook for the motivation, project definition, and discussions.

Acknowledgments: This projects follows pretty closely the template provided by Udacity. We use the VGG19 model by Simonyan and Zissermann (https://arxiv.org/abs/1409.1556) for bottleneck features as well as OpenCV's Haar cascade face detector.

Requirements

The following dependencies are needed:

• Python 3
• Keras with Tensorflow backend
• OpenCV

If you have pip available, you can install them with pip install keras tensorflow opencv-python.

Running the app

After installing the dependencies, simply run python run.py and point you browser to http://0.0.0.0:3001/. The app itself is self-explanatory.

Contents of this repository

• dog_app.ipynb develops the dog breed identification model and contains further discussions about the methods.
• models/ contains saved data for my dog breed identification model (developed in the notebook), as well as a third-party face-detection algorithm.
• dog_app.py is exposes the breed detector as the function my_predict_breed. It also provides the which_breed function which additionally tests whether the picture contains a dog or a human before predicting a breed.
• templates/ contains HTML template files for the webapp.