This project focuses on building and training a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes, including airplanes, cars, birds, cats, etc. The goal is to achieve high accuracy in correctly classifying these images into their respective categories.
The CNN model built for this project consists of several convolutional layers, max pooling layers, and fully connected layers. The model architecture is designed to effectively learn feature representations for the diverse set of images in the CIFAR-10 dataset.
- Python
- TensorFlow
- Keras
- NumPy
- Matplotlib
- Python 3.6 or higher
- TensorFlow 2.x
- NumPy
- Matplotlib
- Clone the repository: