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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
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