🌍 Planets Image Classification using CNN 🚀

🔭 Upload an image of a planet or moon and let a Convolutional Neural Networkidentify the celestial body instantly using a Flask web app.

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📌 Project Overview

This project demonstrates how CNNs (Convolutional Neural Networks) can be used to learn visual features from astronomical images and perform multi-class classification.

The user uploads an image of a planet or moon, and the trained CNN model predicts the correct celestial body.

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✨ Features

• 📤 Upload planet or moon images
• 🧠 CNN-based Deep Learning model
• ⚡ Fast and accurate predictions
• 🖥️ Flask-based web interface
• 🌌 Supports multiple celestial classes

Supported Classes

• Earth
• Jupiter
• Mars
• Mercury
• Moon
• Neptune
• Pluto
• Saturn
• Uranus
• Venus

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🗂️ Dataset

• Source: Kaggle – Planets and Moons Image Dataset
• Images organized into class-wise folders
• Dataset split into training and testing sets

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🧠 Model Details

• Model Type: Convolutional Neural Network (CNN)
• Framework: TensorFlow / Keras
• Language: Python
• Saved Model: planets_and_moons_model.h5

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🛠️ Tech Stack

• Python
• Flask
• TensorFlow / Keras
• NumPy
• OpenCV / PIL
• HTML / CSS

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📁 Project Structure

Planets_and_Moons/
│── static/
│   └── uploads/
│── templates/
│   └── index.html
│── app.py
│── Classification.ipynb
│── planets_and_moons_model.h5
│── requirements.txt
│── README.md

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▶️ How to Run the Project

1️⃣ Clone the Repository

git clone https://github.com/Aryankhanf22/planet-classification.git
cd planets-and-moons-image-classification

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Flask App

python app.py

4️⃣ Open in Browser

http://127.0.0.1:5000/

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🖼️ Screenshots

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📊 Results

• The CNN achieves good accuracy on test images
• Performs well even on visually similar planets
• Generalizes effectively to unseen images

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Open a Pull Request