This project focuses on using Convolutional Neural Networks (CNNs) to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset contains 60,000 32x32 color training images and 10,000 test images, each labeled with one of 10 classes: airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
Python 3.x TensorFlow 2.x or higher NumPy Matplotlib (for plotting results)
The CIFAR-10 dataset is available for download from the Canadian Institute for Advanced Research. The dataset is split into 50,000 training images and 10,000 test images. Each image is 32x32x3 (32 pixels in height and width, 3 color channels: red, green, blue).
The model used in this project is a simple CNN with three convolutional layers, followed by a fully connected layer and a softmax layer for classification. The model is trained using the categorical cross-entropy loss and the Adam optimization algorithm.
The model is trained for a specified number of epochs using the CIFAR-10 training data. The model's accuracy on the test data is evaluated after each epoch, and the final test accuracy is reported.
The model should achieve an accuracy of around 75-88% on the CIFAR-10 test data after training for 20-50 epochs. Results may vary depending on the random initialization of weights and the specific hyperparameters used.
This project demonstrates how CNNs can be used to classify images from the CIFAR-10 dataset with good accuracy. It provides a simple starting point for further experimentation and improvement, such as using deeper networks or more advanced techniques such as data augmentation.