📷 AI-Powered Image Classification App (CIFAR-10)
By Mirza Yasir Abdullah Baig

Artificial Intelligence continues to transform how computers see and interpret the world. One fascinating application of this is image classification — enabling machines to recognize and categorize objects in images. To demonstrate this concept, Mirza Yasir Abdullah Baig developed an AI-powered Image Classification App using Convolutional Neural Networks (CNNs) and Streamlit. This project classifies images into ten distinct categories from the CIFAR-10 dataset, offering an intuitive, real-time demonstration of deep learning.

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🚀 Overview

The Image Classification Web App is a deep learning-based system that classifies uploaded images into one of ten categories:

airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck.

It utilizes a CNN model trained on the CIFAR-10 dataset — one of the most popular datasets for learning computer vision. The app allows users to upload an image, processes it automatically, and provides instant classification results through an interactive web interface built with Streamlit.

🧠 How It Works

1. The user uploads an image (JPG, JPEG, or PNG) to the app.
2. The image is resized and normalized to match the model’s input format (32×32×3 RGB).
3. The trained CNN model (saved as cifar10_cnn_model.keras) processes the image and predicts the most likely category.
4. The app displays the predicted class label instantly — showcasing the model’s confidence and output.

This entire process happens seamlessly within seconds, giving users an engaging and educational experience with AI.

📊 Dataset Details: CIFAR-10

The CIFAR-10 dataset is a benchmark dataset in machine learning and computer vision research.

• Training images: 50,000
• Testing images: 10,000
• Image size: 32×32 pixels, RGB
• Categories (10 classes):
  • Airplane
  • Automobile
  • Bird
  • Cat
  • Deer
  • Dog
  • Frog
  • Horse
  • Ship
  • Truck

This dataset enables models to learn to recognize various real-world objects despite small image sizes and limited features — making it an excellent training ground for CNNs.

⚙️ Tech Stack

• Python 3.9+ – Core programming language
• Streamlit – For building the interactive user interface
• TensorFlow / Keras – For deep learning and CNN model training
• NumPy & Pandas – Data manipulation and processing
• Matplotlib & Seaborn – Visualization and performance analysis
• PIL (Pillow) – Image loading and preprocessing

The combination of these tools ensures an efficient workflow — from model training to visualization and deployment.

🌟 Key Features

• 📂 Upload any image directly to the app
• 🧠 Real-time prediction using CNN
• 🖼️ Supports 10 image categories
• ⚡ Instant and accurate classification
• 🎨 Interactive UI with sidebar navigation
• 👨‍💻 Developer portfolio and contact links

💡 Model Architecture

The Convolutional Neural Network (CNN) is the backbone of this project. It includes:

• Convolutional layers to detect spatial patterns and features.
• Pooling layers to reduce dimensionality and prevent overfitting.
• Dense layers for classification.
• Softmax activation for predicting probabilities across 10 classes.

The model is trained for multiple epochs using categorical cross-entropy loss and an Adam optimizer, ensuring efficient convergence.

🔍 Usage

1. Open the app in your browser.
2. Upload an image file (.jpg, .jpeg, .png).
3. Wait for preprocessing and classification.
4. Instantly view the predicted category (e.g., “dog,” “airplane”).

Whether you are testing animal photos or car images, the system quickly identifies the category with impressive precision.

📸 Screenshots

• 🏠 Home Page – Simple layout and welcome message
• 📷 Image Upload Section – Choose and upload any image
• 🔎 Prediction Display – Shows the detected class label instantly

👨‍💻 About the Developer

Mirza Yasir Abdullah Baig is a passionate AI and ML developer who specializes in building intelligent, interactive web applications using Python and Streamlit. His projects bridge the gap between research and practical implementation — making machine learning accessible to everyone.

🔗 Portfolio Links:
🌐 Kaggle | 💼 LinkedIn | 💻 GitHub

❤️ Acknowledgements

• CIFAR-10 Dataset (Krizhevsky et al.)
• TensorFlow/Keras Documentation
• Streamlit Framework
• Scikit-learn

⚠️ Disclaimer

This project is created purely for educational purposes.
It demonstrates how CNNs can classify images but is not intended for production or commercial deployment. For professional applications, additional data augmentation, fine-tuning, and performance testing are recommended.

🎯 Conclusion

The Image Classification App (CIFAR-10) by Mirza Yasir Abdullah Baig is a remarkable demonstration of deep learning in action. It showcases how convolutional neural networks can accurately classify objects from simple images, turning complex AI concepts into an easy-to-use web experience.

With just an image upload, users can witness how machines “see” and “think” — proving that the future of computer vision is already here.