In this project, I have built a Convolutional Neural Network (CNN) that can classify the breed of dog from any user-supplied image. If the image is of a human and not a dog, the algorithm will provide an estimate of the dog breed that is most resembling.
The code is written in Python 3 and Keras with Tensorflow backend all presented in Jupyter Notebook. I used AWS EC2 gpu instance for training the model.
If you'd like to use this notebook to do any re-training on the dataset you can grab it at the links below.
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Clone the repository and navigate to the downloaded folder.
git clone https://github.com/subhayu99/dog_breed_classifier.git cd dog_breed_classifier
NOTE: if you are using the Udacity workspace, you DO NOT need to re-download the datasets in steps 2 and 3 - they can be found in the /data folder as noted within the workspace Jupyter notebook.
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Download the dog dataset. Unzip the folder and place it in the repo, at location
path/to/dog-project/dogImages. ThedogImages/folder should contain 133 folders, each corresponding to a different dog breed. -
Download the human dataset. Unzip the folder and place it in the repo, at location
path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. -
Make sure you have already installed the necessary Python packages according to the README in the program repository.
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Open a terminal window and navigate to the project folder. Open the notebook and follow the instructions.
jupyter notebook dog_app.ipynb
NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included.