Jayanth-MKV/Landmark-Classification-Tagging-for-Social-Media

Photo sharing and photo storage services like to have location data for each photo that is uploaded. With the location data, these services can build advanced features, such as automatic suggestion of relevant tags or automatic photo organization, which help provide a compelling user experience.

Project Overview

Welcome to the Convolutional Neural Networks (CNN) project! This project, is a pipeline to process real-world, user-supplied images and to put your model into an app. Given an image, your app will predict the most likely locations where the image was taken.

Why We're Here

Photo sharing and photo storage services like to have location data for each photo that is uploaded. With the location data, these services can build advanced features, such as automatic suggestion of relevant tags or automatic photo organization, which help provide a compelling user experience. Although a photo's location can often be obtained by looking at the photo's metadata, many photos uploaded to these services will not have location metadata available. This can happen when, for example, the camera capturing the picture does not have GPS or if a photo's metadata is scrubbed due to privacy concerns.

If no location metadata for an image is available, one way to infer the location is to detect and classify a discernable landmark in the image. Given the large number of landmarks across the world and the immense volume of images that are uploaded to photo sharing services, using human judgement to classify these landmarks would not be feasible.

Getting started

You can work locally on your machine (NVIDIA GPU highly recommended)

Setting up locally

This setup requires a bit of familiarity with creating a working deep learning environment. While things should work out of the box, in case of problems you might have to do operations on your system (like installing new NVIDIA drivers) that are not covered in the class. Please do this if you are at least a bit familiar with these subjects, otherwise please consider using the provided Udacity workspace that you find in the classroom.

1. Open a terminal and clone the repository, then navigate to the downloaded folder:

2. Create a new conda environment with python 3.7.6:

       conda create --name cnn_project -y python=3.7.6
       conda activate cnn_project
   

   NOTE: you will have to execute conda activate cnn_project for every new terminal session.

3. Install the requirements of the project:

       pip install -r requirements.txt
   

4. Install and open Jupyter lab:

       pip install jupyterlab
   	jupyter lab
   

Dataset Info

The landmark images are a subset of the Google Landmarks Dataset v2.