Dog-Breed-Classifier

Project Overview

This project is a part of Udacity's Deeplearning Nanodegree Program. In this project, I have trained a CNN from scratch as well as using Transfer Learning such that given an image of a dog, my model can identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Project Instructions

Instructions

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

   	https://github.com/devangsharma14/Dog-Breed-Classifier.git
   	cd devangsharma14/Dog-Breed-Classifier
   

2. Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. The dogImages/ folder should contain 133 folders, each corresponding to a different dog breed.

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. Make sure you have already installed the necessary Python packages according to the README in the program repository.

5. Open a terminal window and navigate to the project folder. Open the notebook and follow the instructions.

   	jupyter notebook dog_app.ipynb
   

(Optionally) Accelerating the Training Process

If your code is taking too long to run, you can switch to running your code on a GPU. If you'd like to use a GPU, you can spin up an instance of your own:

Amazon Web Services

You can use Amazon Web Services to launch an EC2 GPU instance. (This costs money)