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.

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.