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Project Overview

In this project built a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, the algorithm identifies an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Sample Output

Along with exploring state-of-the-art CNN models for classification, were made an important design decisions about the user experience for the app. The goal is that by completing this project, we've got a list of challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has it's strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer.

Project Instructions

  1. Clone the repository and navigate to the downloaded folder.

    	git clone https://github.com/olpotkin/dog-project.git
	cd dog-project

  2. Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages.

  3. 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.

  4. Donwload the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features.

  5. 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"

  6. Open the notebook and follow the instructions.

    	jupyter notebook dog_app.ipynb

Amazon Web Services (AWS)

Instead of training your model on a local CPU (or GPU), you could use Amazon Web Services to launch an EC2 GPU instance.