Dog Breed Classifier

As part of my Udacity Machine Learning Nanodegree, I learned how to build a pipeline to process real-world, user-supplied images. Given an image of a dog, my algorithm identified an estimate of the canine's breed. If it is supplied with an image of a human, the code identifies the dog breed that human most closely resembles.

See my implementation and report here.

Project Brief

Welcome to the Convolutional Neural Networks (CNN) project! In this project, you will learn how to build a pipeline 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!

Project

My project was evaluated against the Dog Breed Classifier project rubric.

Files Submitted

• The dog_app.ipynb notebook file with all questions answered and all visualization cells executed and displaying results.
• The test-images folder which contains six images that my CNN was tested on.
• An HTML export of the project notebook with the name report.html.