This project demonstrates image classification using a Convolutional Neural Network (CNN) built with TensorFlow and Keras on the CIFAR-10 dataset. CIFAR-10 contains 60,000 32x32 RGB images across 10 categories such as airplanes, cars, birds, cats, and dogs.
The notebook image-classification-CNN-cifar10.ipynb guides you through:
- Loading and preprocessing the CIFAR-10 dataset
- Designing and training a CNN model
- Evaluating model performance with accuracy and loss metrics
- Visualizing predictions on sample images
This project showcases how deep learning can automatically learn image features and make accurate predictions, providing a practical introduction to CNNs and TensorFlow for image recognition.
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├── image-classification-CNN-cifar10.ipynb # Main notebook
├── README.md # Project description
└── requirements.txt (optional) # Python dependencies
git clone https://github.com/your-username/cnn-cifar10.git
cd cnn-cifar10
pip install -r requirements.txt
jupyter notebook image-classification-CNN-cifar10.ipynb- Data augmentation for better generalization
- Deeper CNN architectures (ResNet, VGG)
- Hyperparameter tuning
- Deployment as a web API with Flask/FastAPI
Python 3.8+, TensorFlow, Keras, NumPy, Matplotlib, Jupyter Notebook
MIT License