CIFAR-10 Image Classification Web App

This project demonstrates how to build an image classification web application using the CIFAR-10 dataset, TensorFlow, and Flask. The web application allows users to upload images and classify them into one of 10 categories.

Features

• Train a Convolutional Neural Network (CNN) on the CIFAR-10 dataset.
• Evaluate the model on test data.
• Save and load the trained model.
• Upload images through a web interface and classify them.
• Display classification results with corresponding images.

Installation

1. Clone the repository:

   git clone https://github.com/devhadvani/image-classification-keras-flask.git
   cd image-classification-keras-flask

2. Install dependencies:

   pip install -r requirements.txt

Usage

Train the Model

Run the script to train the CNN model on the CIFAR-10 dataset:

```bash
python train_model.py
```

The trained model will be saved as cifar10_model.h5.

Run the Web Application

1. Start the Flask web application:

   python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000/.

3. Use the web interface to upload images and classify them.

Directory Structure

```plaintext
cifar10-image-classification-web-app/
│
├── app.py                 # Flask web application
├── train_model.py         # Script to train the CNN model
├── requirements.txt       # Python dependencies
├── templates/
│   └── index.html         # HTML template for the web interface
└── static/
    └── uploads/           # Directory to save uploaded images
```

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

This project is licensed under the MIT License.