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Vehicle Image Similarity Search using Deep Learning and PySpark

Overview

This project focuses on finding similar vehicle images using deep learning and PySpark. It involves extracting image embeddings using a pre-trained ResNet-18 model, storing these embeddings, and performing a similarity search using Locality-Sensitive Hashing (LSH) in PySpark. The goal is to identify the most similar vehicles based on their image features.

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Data Source: The project uses the Stanford Cars Dataset, which contains images of cars from different classes..

Table of Contents

Project Features

  • Image Embedding Extraction: Uses a pre-trained ResNet-18 model to extract feature vectors (embeddings) from vehicle images.
  • Locality-Sensitive Hashing (LSH): Implements LSH in PySpark to efficiently find similar images based on their embeddings.
  • Scalable Similarity Searchr: Handles large datasets by leveraging PySpark's distributed computing capabilities.
  • Show Results: Displays the most similar images for a given query image.

Project Architecture

Below is the high-level architecture of the project, including image acquisition, preprocessing, digit recognition, solution, and visualization stages.

Project Architecture Diagram

  1. Image Preprocessing: Resizes and normalizes images to prepare them for feature extraction.
  2. Feature Extraction:: Uses a pre-trained ResNet-18 model to extract 512-dimensional feature vectors from the images.
  3. LSH Indexing: Applies Locality-Sensitive Hashing (LSH) to index the feature vectors for efficient similarity search.
  4. Similarity Search: Queries the LSH index to find the most similar images for a given input image.
  5. Results Visualization: Displays the query image and its nearest neighbors.

Technologies Used

  • Python: Core programming language.
  • PyTorch: For loading and using the pre-trained ResNet-18 model.
  • PySpark: For distributed computing and implementing LSH.
  • Pillow/Pandas: For image handling and manipulation and storage.

Running the Application

  1. Clone the Repository:

    • First, clone the repository to your local machine: 
      git clone https://github.com/Carlos93U/Vec2Car.git
cd Vec2Car

  2. Create and Activate Virtual Environment:

    • It is recommended to create a virtual environment to manage the dependencies: 
      python -m venv env
source env/bin/activate  # Linux/Mac
.\env\Scripts\activate  # Windows

  3. Run first cell on notebook to install dependencies:

    • Install the required dependencies running first cell on notebook: 
      !pip install pandas
!pip install pyspark
!pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
!pip3 install tqdm

  4. Run all cells on notebook:

    • Run all cells on notebook to extract embeddings and perform similarity search:

Project Structure

The following is an overview of the main folders and files within the project.

Vec2Car/
├── data/                      # Data files and images
│   ├── car_dataset_test.csv
│   ├── car_dataset_train.csv
│   ├── input_data_vectors.csv
│   ├── cars_test/
│   ├── cars_train/
│   └── input_images_cnn/
│
├── notebooks/                  # Notebook for data preprocessing and model training
├── docs/                       # Project documentation
└── README.md                   # Project documentation

Contact

For any questions or feedback, please contact:

Juan C. Huillcas
Email: huillcas.juan3@gmail.com
LinkedIn: Juan C. Huillcas