KEVAL RAVANI

🌟 Image Recognition with CIFAR-10 Dataset 🌟

![CIFAR-10]

📜 Overview

Welcome to the Image Recognition Project by Keval Ravani! 🚀
This project uses a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset, which contains 60,000 32x32 color images across 10 classes, such as 🛩️ airplanes, 🚗 automobiles, 🐦 birds, 🐱 cats, and more.

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📂 Project Structure

The project follows a well-defined pipeline:

1. Dataset Loading: CIFAR-10 dataset is loaded using TensorFlow.
2. Preprocessing: Images are normalized for better model performance.
3. Model Construction: A CNN is built using TensorFlow/Keras.
4. Training: The model is trained on the dataset.
5. Evaluation: The model's accuracy is tested on unseen data.
6. Visualization: Random samples are visualized with predictions.

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🎯 Features

• 🔍 Image classification using CNNs
• 📊 Model training and evaluation
• 🖼️ Visualization of sample predictions
• 🏷️ Classes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck

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📦 Requirements

Dependencies

Ensure you have the following Python libraries installed:

• tensorflow (compatible with Python 3.11)
• matplotlib
• random

Install them using:

Python Version

This project requires Python 3.11.x for compatibility with TensorFlow.

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🛠️ Installation

1. Clone the repository:
2. Install dependencies:
3. Run the script:

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📖 Code Walkthrough

1️⃣ Dataset Loading and Preprocessing

The CIFAR-10 dataset is loaded and split into training and testing sets: Images are normalized to scale pixel values between 0 and 1:

2️⃣ Visualization of Data Samples

Random samples from the training set are visualized with their labels:

3️⃣ CNN Model Architecture

The CNN consists of three convolutional layers followed by max-pooling layers and dense layers:

4️⃣ Training and Evaluation

The model is compiled and trained for two epochs: Evaluation on the test set:

5️⃣ Prediction and Visualization of Results

Random samples from the test set are visualized with their actual and predicted labels:

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📊 Results

Metric	Value
Training Accuracy	~59%
Test Accuracy	~60%

Sample Output Visualization:

Actual Label: 🐸 Frog

Predicted Label: 🐸 Frog

📈 Future Enhancements

• Train the model for more epochs to improve accuracy.
• Experiment with data augmentation techniques.
• Implement transfer learning using pre-trained models like ResNet or VGG.

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👨‍💻 Author

Keval Ravani
💌 Email: kevalravani06@gmail.com
📅 Date: April 13, 2025

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📜 License

This project is licensed under the MIT License.

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✨ Acknowledgments

Special thanks to the TensorFlow team for providing an easy-to-use framework and to the creators of the CIFAR-10 dataset.

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🚀 Let's Collaborate!

Feel free to open issues or submit pull requests for improvements. Contributions are always welcome! 😊

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