Prediction example The probability plot

Landmark Tagging For Social Media

Machine Learning
PyTorch
Python

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.

Sample results

The images below display some sample outputs of my finished project (on the left is top three probabilities):

Sydney_Harbour_Bridge Trevi_Fountain Death_valley2 Gateway_of_India

Dataset

The landmark images are a subset of the Google Landmarks Dataset v2. It can be downloaded using this link You can find license information for the full dataset on Kaggel