🧠 CIFAR-10 Image Classifier Web App

This project is a web-based image classification tool built with TensorFlow and Streamlit. It uses a Convolutional Neural Network (CNN) to classify images into one of 10 categories from the CIFAR-10 dataset.

🌐 Live Demo: Click here to open the Streamlit app

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📌 Features

• Upload any image file for prediction
• Pretrained CNN model on CIFAR-10 dataset
• Predicts image class with confidence score
• Clean, minimal UI using Streamlit

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🧠 Model Details

• Input Shape: 32x32x3
• Architecture:
  • Conv2D + BatchNorm + MaxPooling + Dropout
  • Fully connected Dense layers
  • Output: 10-class softmax
• Optimizer: Adam (lr = 0.0005)
• Loss Function: Categorical Crossentropy
• Test Accuracy: ~79%

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🔢 CIFAR-10 Classes

0 - airplane 1 - automobile 2 - bird 3 - cat 4 - deer 5 - dog 6 - frog 7 - horse 8 - ship 9 - truck

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📁 Project Structure

image_classifier/ ├── app.py # Streamlit app ├── predict.py # Model loading and prediction ├── cifar10cnn_model.h5 # Trained model ├── requirements.txt # Main dependencies ├── packages.txt # Additional packages for deployment └── README.md # This file

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🚀 Run the App Locally

git clone https://github.com/Logicrithm/image_classifier.git
cd image_classifier

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py