This project is an image classification system built using the CIFAR-10 dataset. It demonstrates the process of loading and preprocessing the data, building a Convolutional Neural Network (CNN) model, training, evaluating the model, and visualizing the results.
A classification model to identify images from 10 different classes: airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. The goal is to achieve high accuracy using a CNN built with TensorFlow and Keras.
The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.
The CNN model consists of:
- 3 Convolutional Layers with ReLU activation.
- MaxPooling layers for downsampling.
- A Fully Connected Layer with 128 neurons.
- A Dropout Layer to prevent overfitting.
- An output layer with softmax activation for classification.
After training the model for 10 epochs, we achieve a test accuracy of approximately 70.34%.