This project was part of the Udacity Nanodegree Program.
An algorithm to identify canine breed given an image of a dog. If given image of a human, the algorithm identifies a resembling dog breed.
You also need the dogImages dataset to train the network.
Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build 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, your algorithm will identify 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, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience!
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Prerequisite Python modules to run the code: sklearn, keras, numpy, cv2, matplotlib, tqdm, PIL
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Clone the repository and navigate to the downloaded folder.
git clone https://github.com/discimus-scientia/dog_breed.git
cd dog-project
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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. -
The notebook should now run smoothly.