🚗 Vehicle Classification

This project is a deep learning-based vehicle classification system built using TensorFlow/Keras.
It allows you to train, evaluate, and deploy a model that classifies different types of vehicles from images.
The project includes a saved model (.h5), a training notebook, and a simple application script (app.py) for running predictions.

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📂 Project Structure

Vehicle-Classification/
│
├── Vehicle Classification.ipynb   # Jupyter notebook for training & evaluation
├── app.py                          # Application script to load model & classify images
├── vehicle\_classification\_model.h5 # Pre-trained Keras model
├── requirements.txt                # Python dependencies
├── data/                           # Dataset (images of vehicles)
├── logs/                           # Training logs & history
└── README.md                       # Project documentation (you are here)

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⚙️ Installation

1. Clone the repository:

git clone https://github.com/Kalika-Jay/Vehicle-Classification.git
cd Vehicle-Classification

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

python -m venv venv
source venv/bin/activate   # On Linux/Mac
venv\Scripts\activate      # On Windows

3. Install dependencies:

pip install -r requirements.txt

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🚀 Usage

1. Training the Model

Open and run the notebook:

jupyter notebook "Vehicle Classification.ipynb"

This will:

• Load the dataset from data/
• Preprocess and augment images
• Train the CNN model
• Save the trained model as vehicle_classification_model.h5

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2. Running Predictions with app.py

The app.py script loads the saved model and predicts the class of a given image.

python app.py --image path_to_image.jpg

Example output:

Predicted Class: Car

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🧠 Model Details

• Framework: TensorFlow/Keras
• Type: Convolutional Neural Network (CNN)
• Input size: (check notebook, usually 128x128 or 224x224)
• Output: Predicted vehicle category (Car, Bus, Truck, etc.)

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📊 Logs

Training logs are saved in the logs/ folder for further analysis and visualization (e.g., with TensorBoard).

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✅ To-Do

• Add more vehicle categories
• Improve dataset balance
• Deploy as a Flask/Django web app
• Containerize with Docker