🪟 Image Classification Using Neural Networks

This project demonstrates image classification using Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) with the CIFAR-10 dataset. It includes dataset preprocessing, model building, training, and evaluation.

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✨ Table of Contents

1. Introduction
2. Dataset
3. Technologies Used
4. Project Structure
5. How to Run
6. Code Explanation
7. Results
8. Conclusion

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📝 Introduction

Image classification is the process of identifying and categorizing objects in an image. In this project, we use two types of neural networks:

• ANN (Artificial Neural Network): A basic model for image classification.
• CNN (Convolutional Neural Network): A more advanced model that captures image-specific features for better accuracy.

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💾 Dataset

We use the CIFAR-10 dataset, which contains:

• 60,000 images divided into 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.
• Images are 32x32 pixels with 3 color channels (RGB).

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💻 Technologies Used

• Python: Programming language.
• TensorFlow/Keras: Framework for building and training neural networks.
• Matplotlib: Library for data visualization.
• NumPy: Library for numerical computations.

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🗂️ Project Structure

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├── README.md # Documentation
└── Image_Classification_(Cifar_10).ipynb # Main script to execute the project

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⚡ How to Run

1. Clone the repository:

   git clone https://github.com/DevSumanP/ImageClassifier101.git
   cd ImageClassifier101

2. Install dependencies:

   pip install tensorflow matplotlib numpy

3. Run the script:

   python main.py

4. Expected Output:

   • Model training logs.
   • Accuracy metrics for both ANN and CNN.
   • Visualizations of predictions.

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⚡ Code Explanation

1. Data Preprocessing:

   • Normalize pixel values to range [0, 1] for faster and stable training.
   • Reshape labels to make them compatible with the models.

2. ANN Model:

   • A simple architecture with fully connected layers.
   • Output layer uses a softmax activation function to predict probabilities for 10 classes.

3. CNN Model:

   • Includes convolutional and pooling layers to extract features from images.
   • Improves classification accuracy compared to ANN.

4. Evaluation:

   • Evaluate models using test data and generate classification reports.

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😃 Results

Model	Accuracy
ANN	~49%
CNN	~70%

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Conclusion

• ANN is suitable for simple datasets but lacks accuracy for images.
• CNN performs better due to its ability to extract spatial hierarchies (e.g., edges, textures).
• Image classification is an essential task in AI, with applications in self-driving cars, medical diagnostics, and more.

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Future Work

• Use data augmentation to improve accuracy.
• Experiment with deeper networks or transfer learning using pre-trained models.
• Apply this framework to a custom dataset for real-world applications.

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🗒️ License

This project is open-source under the MIT License.