An AI-powered web application built with Streamlit that classifies images into one of the 10 CIFAR-10 categories using a Convolutional Neural Network (CNN) trained on the CIFAR-10 dataset.
screen-capture.14.webm
- Upload an image and get instant classification results.
- Model predicts among 10 categories:
airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
- Powered by a deep learning CNN model trained on CIFAR-10.
- Simple and interactive Streamlit interface.
- Sidebar with developer details and portfolio links.
- Open the app in your browser.
- Upload an image (
.jpg,.jpeg,.png). - The model will preprocess and classify it.
- Get the predicted class label instantly.
The model is trained on the CIFAR-10 dataset:
- Training Data: 50,000 images
- Test Data: 10,000 images
- Image Dimensions: 32×32 pixels, RGB
- Classes (Labels):
- airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
- Python 3.9+
- Streamlit (Web App UI)
- TensorFlow / Keras (Deep Learning Model)
- NumPy & Pandas (Data Processing)
- Matplotlib & Seaborn (Visualization & Confusion Matrix)
- PIL (Pillow) (Image Handling)
Mirza Yasir Abdullah Baig
This project is for educational purposes only.
It is not intended for production or commercial deployment, but as a demonstration of deep learning for image classification.