| # 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 | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| Run the Model | |
| ```bash | |
| python app.py | |
| ``` |