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#Deep Learning - Pugs vs. Chihuahuas.

This is the github repository for the Pug/Chihuahua classifier model and flask web app. The project was inspired by Michelangelo D'Agostino's Strata 2016 talk and the VGG16 pretrained model developed for the ILSVRC-2014 competition. See for more information.

#Get the Data The first step is to download the images from ImageNet. If you have a university email address, you can download the images directly.
If you do not, see the data folder for a list of url's of pug and chihuahua images. Write a script to download the images. Then, crop all images to 224 x 224 pixels using imagemagick (brew install imagemagick). You will need imagemagick to use the flask app as well.

#Make the Model Make sure you have the following packages installed: scipy, numpy, scikit-image, requests, h5py, flask, keras, theano.
Download the vgg16 weights, "vgg16.h5", available at the following github page Now run pugs_vs_chihuahuas.ipynb to build the model and evaluate_pugs_vs_chihuahuas.ipynb to evaluate the model. Important! This model takes quite a long to build on a macbook pro (but not an amazon ec2 GPU).

#Run the Flask App Copy the weights produced from building the model to the flask-app folder and run python in the terminal. Open your browser to the appropriate local ip address to view and use the flask app. Try running the app on examples of pug/chihuahua mixes as well.

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