CIFAR – 10 prediction

Keywords: CIFAR – 10 prediction

Background

The CIFAR-10 dataset is a widely used benchmark in the field of computer vision, consisting of 60,000 color images categorized into ten distinct classes. Accurate prediction of image classes in CIFAR-10 is a challenging task due to the dataset's complexity and variability. This study proposes an approach for CIFAR-10 prediction using Convolutional Neural Networks (CNN). The research utilizes a deep learning architecture based on CNNs to extract relevant features and classify images into their respective classes. The model undergoes a comprehensive training process using the CIFAR-10 dataset, encompassing data augmentation techniques to improve generalization and reduce overfitting.

Aim & Objectives

Aim
To predict CIFAR – 10Using DL Technique
Objective
Develop and optimize a Convolutional Neural Network (CNN) approach for accurate classification of CIFAR-10 images, investigating the impact of various network architectures, hyperparameters, and optimization algorithms on prediction performance. The study aims to enhance the understanding of image recognition techniques, providing insights into achieving optimal accuracy and contributing to broader applications in fields like object detection, autonomous driving, and facial recognition.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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