image-retrieval

This repository presents an image retrieval pipeline that consists of a vector database storing image features extracted using a pre-trained ResNet/ViT model. Image retrieval pipeline consists of (1) a vector database building script that extracts image features of all images in the collection using a pre-trained ResNet/ViT model and stores them in a vector database by cosine similarity, (2) a search query script that extracts the features for a randomly selected image and returns the most similar images from the vector database.

The construction of the proposed image retrieval pipeline can be divided into three stages:

1 - Place your images (a collection of images that you want to create an image retrieval pipeline on) under the data folders as data/images. In this project, Landscape Pictures Dataset is used but feel free to use your of own image collection.
2 - Extract the features of all images using a pre-trained ResNet model and save all features into a vector database.
3 - For a given query image, perform a similarity search by first extracting the features of the query image and then performing a search over the vector database.

Installation

1. Clone the repository to your local first as follows

$ git clone https://github.com/cetinsamet/image-retrieval.git

2. Then recreate the virtual environment on your local using the provided .yml file and activate the venv as follows

$ cd image-retrieval  
$ conda env create -f environment.yml
$ conda activate image-retrieval

3. Add the path of your own working directory as WORK_DIR in the configuration file src/config/settings.pyas follows

$ cd src
$ nano config/settings.py  # or use your favorite text editor and add the path of your own working directory

WORK_DIR = "PATH-TO-YOUR-OWN-WORKING-DIRECTORY"

4. Create a data folder as follows

$ mkdir ../data

5. Download data from here (Landscape Pictures Dataset) and place all image files under data/images folder.

Run

Please perform following steps sequentially to reproduce the image retrieval pipeline that gives the search query examples below.

Building vector database

Running build_vector_database.py extracts the features of all downloaded images by using the selected pre-trained ResNet model (please use --feat_extractor flag to customize) and save the features into a vector database that is stored under data/ directory.

$ python3 build_vector_database.py --feat_extractor vit_l_32

Search query

Finally, running search_query.py selects random object images (the amount of the selected images can be customized using --n flag) and search the most similar object images (the amount of the similar images can be customized using --k flag) by querying the pre-built vector database.

$ python3 search_query.py --feat_extractor vit_l_32 --n 10 --k 12

Some examples of the resulting search queries are as follows;