Deep Learning Nanodegree

Deep Learning

Project: Landmark Classification & Tagging for Social Media

Project for Udacity's Deep Learning Nanodegree program. In this project, I developed a deep learning model using Pytorch to detect and classify landmarks.

In order to complete this project, I used the GPU enabled workspaces within the Udacity classroom.

Install

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• glob
• cv2
• matplotlib (2.0.2)
• tqdm
• torch (1.9.0)
• torchvision (0.10.0)
• PIL
• os
• random
• math

You will also need to have software installed to run and execute an iPython Notebook

I recommend installion Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

Template code is provided in the landmark.ipynb file.

Run

In a terminal or command window, navigate to the top-level project directory Landmark-Classification-Tagging-for-Social-Media/ (that contains this README) and run one of the following commands:

ipython notebook landmark.ipynb

or

jupyter notebook landmark.ipynb

This will open the iPython Notebook software in your browser.

Data

The landmark images used for this project are a subset of the Google Landmarks Dataset v2. The dataset used for this project is quite large and can be downloaded by following this link.

You can find license information for the full dataset on Kaggle.

Certification