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Clone the repository and navigate to the downloaded folder.
git clone https://github.com/ysharc/dog-breed-classifier.git cd dog-breed-classifier
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Download the dog dataset. Unzip the folder and place it in the repo, at location
path/to/dog-project/dogImages
. -
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. -
Donwload the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location
path/to/dog-project/bottleneck_features
. -
Obtain the necessary Python packages, and switch Keras backend to Tensorflow.
For Mac/OSX:
conda env create -f requirements/aind-dog-mac.yml source activate aind-dog KERAS_BACKEND=tensorflow python -c "from keras import backend"
For Linux:
conda env create -f requirements/aind-dog-linux.yml source activate aind-dog KERAS_BACKEND=tensorflow python -c "from keras import backend"
For Windows:
conda env create -f requirements/aind-dog-windows.yml activate aind-dog set KERAS_BACKEND=tensorflow python -c "from keras import backend"
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Open the notebook and follow the instructions. I recommend running this with GPU support.
jupyter notebook dog_app.ipynb
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Classifying 133 dog breeds using transfer learning and CNN
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