📷 DigiPic-Classifier - Advanced Image & Digit Recognition App

Welcome to DigiPic-Classifier, an all-in-one image recognition and digit classification app powered by advanced machine learning models. Whether you need to classify objects or recognize handwritten digits, DigiPic-Classifier has you covered!

🌟 Features

• Multi-Model Image & Digit Recognition:

  • CIFAR-10 Object Recognition: Recognizes 10 different objects including airplanes, automobiles, birds, cats, and more! 🛩️🚗🐱
  • MNIST Digit Classifier: Accurately predicts handwritten digits from 0 to 9. 🧮

• Interactive & Intuitive UI: 🖥️ A modern, sleek user interface designed for easy navigation and enhanced user experience, with a dark theme option and custom animations.

• Real-time Predictions: 💡 Upload your image and get an instant prediction with the corresponding confidence score.

• Model Comparison: 📊 Evaluate the performance of both models through accuracy metrics and confidence levels for each prediction.

• Advanced Technology: Leveraging cutting-edge machine learning algorithms including CNNs (Convolutional Neural Networks) for high accuracy image and digit predictions.

🔥 Live Demo

• CIFAR-10 Object Recognition:

• MNIST Digit Classifier:

🖼️ Preview

CIFAR-10 Object Recognition

MNIST Digit Classification

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🚀 How to Use DigiPic-Classifier

1. CIFAR-10 Object Recognition App

1. Clone the Repository:

   git clone https://github.com/Hunterdii/DigiPic-Classifier.git

2. Navigate to CIFAR-10 App Directory:

   cd DigiPic-Classifier/Cifar_10-Object-Recognition

3. Install the Required Dependencies:

   pip install -r requirements.txt

4. Run the CIFAR-10 Streamlit App:

   streamlit run app.py

5. Open the App: Open your browser and go to http://localhost:8501 to use the CIFAR-10 Object Recognition app.

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2. MNIST Digit Classification App

1. Clone the Repository:

   git clone https://github.com/Hunterdii/DigiPic-Classifier.git

2. Navigate to MNIST App Directory:

   cd DigiPic-Classifier/MNIST-Classification

3. Install the Required Dependencies:

   pip install -r requirements.txt

4. Run the MNIST Streamlit App:

   streamlit run app.py

5. Open the App: Open your browser and go to http://localhost:8501 to use the MNIST Digit Classification app.

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🎨 Customization

You can personalize the app by modifying the CSS for styling, enhancing the user interface, or updating the models. The repository includes well-documented code, making it easy to navigate, tweak, and extend functionality.

📦 Models in the App

1. CIFAR-10 Object Recognition

• Recognizes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck.
• Prediction Speed: Fast real-time results with high accuracy.

2. MNIST Digit Classification

• Recognizes: Handwritten digits (0-9).
• Versatile: Ideal for digit recognition tasks in educational or professional settings.

📈 Future Enhancements

• Adding more sophisticated image classification models.
• Deploying MNIST Classifier live for broader accessibility.
• Implementing additional UI improvements and advanced animations.