CIFAR-10 Image Classification App

This project uses a deep learning model built with TensorFlow/Keras to classify images from the CIFAR-10 dataset. It includes a user-friendly Streamlit interface where users can upload an image and see classification results with prediction probabilities.

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Project Features

• CNN Model Training on the CIFAR-10 dataset
• Data Augmentation using ImageDataGenerator
• Callbacks for early stopping and learning rate reduction
• Model Saving in .h5 format
• Streamlit Interface for real-time image classification
• Visualization of training accuracy and confusion matrix

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Technologies Used

• TensorFlow / Keras
• NumPy, Matplotlib, Seaborn
• Streamlit
• scikit-learn

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• Example Output

• Predictions:

1. frog (92.4%)
2. cat (3.1%)
3. deer (2.0%) -- Project Structure

Cifar-10-Classification/

├── app.py # Streamlit interface

├── model.ipynb # Model training script

├── cifar10_gelismis_model.h5 # Trained model

├── README.md # Project documentation Developer Seda Ozkaya GitHub: sedaozkaya