Image Classification Using CNN with CIFAR-10 Dataset

Abstract: Image classification is a very important issue in digital image analysis. CNN is type of Deep Neural Networks (DNN) that contains multiple layerssuch as Conv layers, integration layer and fully integrated layer. Convolutional neural networks (CNNs) are widely used in pattern-and picture- recognition problems as they have a number of advantages compared to others strategies. CIFAR-10is a very popular computer vision database. Thisdatabase is well read in it many types of in-depth study of object recognition. This database contains 60,000 images separated by 10 target classes, each a section containing 6000 images of 32 * 32 shapes. This database contains images of low-resolution (32 * 32), which allows researchers to experiment with new algorithms. Image classification is a fundamentaltask in computer vision that involves assigning a labelor category to an image. Convolutional Neural Networks (CNNs) have become the state-of-the-art method for image classification due to their ability toautomatically learn features from raw pixel values. Inthis project, we aim to build a CNN model that can accurately classify images in the CIFAR-10 dataset.