Navigating the visual complexity: A deep dive into cifar-10 enhancement using resnet-50

Abstract

This study explores the enhancement of object recognition by employing Resnet-50, a deep convolutional neural network architecture. The investigation is centered on the CIFAR-10 dataset with the objective of improving accuracy and efficiency in object recognition tasks. Resnet-50 is examined as a potent tool for feature extraction and classification within the intricate visual data of CIFAR-10. Through rigorous experimentation and analysis, this research seeks to reveal insights into the model's performance, pinpoint areas for improvement, and contribute to the continual refinement of object recognition methodologies.