Car Classification

Overview

This project is a web application that uses deep learning to classify car images. Users can upload photos of cars, and the system will identify the car model using a pre-trained ResNet50 model.

Features

• Real-time car image upload and preview
• Instant car model prediction
• User-friendly interface
• Responsive design for all devices

Technologies Used

• Frontend:
  • HTML5
  • CSS3
  • JavaScript
• Backend:
  • Python
  • Flask
• Machine Learning:
  • PyTorch
  • Pre-trained ResNet50 model

Project Structure

deep-learning-project/
├── app.py 
├── static/
│   ├── js/
│   │   └── deepcars.js # JavaScript functions
│   ├── css/
│   │   └── style.css # Styling
│   └── imgs/ # Static images
├── templates/
│   └── deepCars.html # Main HTML template
├── requirements.txt # List of dependencies

Installation

Follow the steps below to set up the project:

1. Clone the repository:

   git clone https://github.com/AhmedFathyElrefaey/Car-Classification-Brand.git
   cd Car-Classification-Brand-main

2. Create a virtual environment (optional but recommended):

   python -m venv venv

   Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application:

   python app.py

5. Open your browser and navigate to:

   http://localhost:5000
   

Dataset Paths

Ensure that the paths in the app.py file match your local environment:

• train_path: Path to the training dataset.
• valid_path: Path to the validation dataset.

Usage

1. Open the application in your web browser.
2. Click on the upload button or drag and drop a car image.
3. Wait for the model to process the image.
4. View the prediction results.

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Enjoy using the Car Classification application!