This article summarises my progess in GSoC over week nine of the GSoC coding period.

Implementing the complete framework

This week I was able to finish all the parts of the framework and create a functional pipeline for the process. The steps involved:

  1. Getting an image from the user.
  2. Pass the image through a pre-trained ResNet-50 to generate the embeddings.
  3. Load the ResNet-50 embeddings of images in the dataset created earlier.
  4. Compute similarity between the query image and images in the dataset.
  5. Create a ranked list of dataset images in decreasing order of similarity scores.

Demo of the framework

Based on the pipeline mentioned above, for the purpose of mid-eval, I created a webpage that takes as input an image’s index (to use as query) and queries over rest of the images in the dataset. Here is the link to the code: Github.

Here is an image from the webpage: website_demo