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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