Keys

CNN, Pytorch, VGG16, DeepLearning

Project Overview

In this project, we 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, our 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 and localization, we will make important design decisions about the user experience for our app.

Project Instructions

Instructions

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

   	git clone https://github.com/udacity/deep-learning-v2-pytorch.git
   	cd deep-learning-v2-pytorch/project-dog-classification
   

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
   

Amazon Web Services

You can use Amazon Web Services to launch an EC2 GPU instance. (This costs money, but enrolled students should see a coupon code in their student resources.)