Image Retrieval System using ResNet50 and Nearest Neighbors

This repository contains code for an image retrieval system built using ResNet50, a pre-trained convolutional neural network, and Nearest Neighbors algorithm to find similar images based on feature embeddings.

Overview

The system leverages ResNet50, a powerful deep learning model pre-trained on ImageNet, for extracting image features. These features are stored as embeddings in a pickle file, along with associated filenames.

Files Included

app.py: Python script implementing the image retrieval system.
res_vector_embeddings: Pickle file containing feature embeddings of images.
filenames.pkl: Pickle file storing filenames corresponding to the image embeddings.

Getting Started

Prerequisites

Python 3.10
Dependencies: Keras, NumPy, scikit-learn

You can install the dependencies via:

Requirements installation

pip install -r requirements.txt

Run the Model

python app.py