Image Classification using ANN & CNN

📸 Project Overview

This project focuses on image classification using the CIFAR-10 dataset. Two models, ANN and CNN, are trained on 32x32 color images spanning 10 different classes. The dataset undergoes preprocessing, after which the models are built, trained, and evaluated.

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🚀 Features

• 🔹 Artificial Neural Network (ANN) implementation
• 🔹 Convolutional Neural Network (CNN) implementation
• 🔹 Utilizes TensorFlow and Keras
• 🔹 CIFAR-10 dataset for multi-class classification
• 🔹 Model evaluation & visualization with Matplotlib

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📂 Dataset

The CIFAR-10 dataset consists of 60,000 images (50,000 for training, 10,000 for testing) classified into: 🔹 Airplane ✈️
🔹 Automobile 🚗
🔹 Bird 🐦
🔹 Cat 🐱
🔹 Deer 🦌
🔹 Dog 🐶
🔹 Frog 🐸
🔹 Horse 🐴
🔹 Ship 🚢
🔹 Truck 🚛

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🖥 GUI Preview

An interactive GUI is included for testing the model with custom images.

🔹 Features

• Upload an image for classification
• Get real-time predictions
• Compare ANN vs CNN performance

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📌 Installation & Usage

🔹 Prerequisites

Ensure you have Python installed along with required libraries:

pip install tensorflow numpy matplotlib tkinter

🔹 Clone the Repository

git clone https://github.com/yourusername/image-classification.git
cd image-classification

🔹 Run the Training Script

python train_model.py

🔹 Launch the GUI

python gui.py

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📊 Model Performance

The models are trained and evaluated based on accuracy and loss metrics.

Model	Accuracy (Test)
ANN	65%
CNN	85%

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🤝 Contributing

Feel free to contribute! Open an issue or submit a pull request.

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📜 License

This project is licensed under the MIT License.

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📞 Contact

For any queries, reach out to me at: [email protected]