Project Overview

In this project, I build a Convolutional Neural Networks (CNN) to classify dogs among 133 breeds with 89% accuracy. Given an image of a dog, the 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.

The code is written in Python 3 and PyTorch all presented in this Jupyter Notebook: dog_app.ipynb.

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

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

   	jupyter notebook dog_app.ipynb