International Journal of Electronic Devices and Networking
2024, Vol. 5, Issue 1, Part A
Navigating the visual complexity: A deep dive into cifar-10 enhancement using resnet-50
Author(s): Ashmandeep Kaur and Shivani
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.
DOI: 10.22271/27084477.2024.v5.i1a.47
Pages: 08-14 | Views: 390 | Downloads: 177
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How to cite this article:
Ashmandeep Kaur, Shivani. Navigating the visual complexity: A deep dive into cifar-10 enhancement using resnet-50. Int J Electron Devices Networking 2024;5(1):08-14. DOI: 10.22271/27084477.2024.v5.i1a.47
