Enhancing object recognition with resnet-50: an investigation of the cifar-10 dataset

This study explores object recognition experiments conducted using the CIFAR-10 dataset, a well-established benchmark in machine learning and neuromorphic computing. CIFAR-10 consists of 28x28 grayscale digit images, with 60,000 training samples and 10,000 testing samples, providing a robust evaluation platform for learning algorithms. Object recognition, pivotal in computer vision, has advanced due to deep learning techniques. The study aims to improve object recognition by employing the ResNet-50 architecture on CIFAR-10. ResNet-50, a potent 50-layer convolutional neural network, addresses gradient vanishing issues in deep networks, significantly enhancing object recognition accuracy. This research builds on prior CIFAR-10 studies focused on enhancing ResNet-50's object recognition performance. Utilizing the dataset's 60,000 images across 10 classes pushes object recognition boundaries. In conclusion, this study deepens our understanding of learning algorithms by applying them to CIFAR-10, especially ResNet-50. It underscores object recognition's importance and broad practical applications. Adopting deep learning, exemplified by ResNet-50, can boost object recognition's accuracy and efficiency, impacting domains like autonomous vehicles and image-based search engines.