Veritas AI: CIFAR-10 Image Classification

Registration ID: IJNRD_205256

Published ID: IJNRD2310207

DOI: Click Here to Get

Akshay Akhileshwaran

CIFAR-10, Image classification, Convolution Neural Networks (CNNs), Benchmark dataset, Machine learning, Computer vision

The CIFAR-10 dataset has developed as one of the most prominent benchmarks for evaluating image classification models due to its diverse classes and relatively small image sizes. In this research, the application of deep learning techniques has been explored for enhancing the performance of image classification with the CIFAR-10 dataset. [‎1] Leveraging the power of the Convolutional Neural Networks (CNN), a novel architecture tailored to use this model in Self-Driving Cars is proposed. The current study involves extensive experimentation with different network configurations, hyperparameters, and optimization algorithms to identify the most effective approach. The impact of varying training strategies, including data augmentation and transfer learning on robustness and model generalization have been analyzed. [‎8] Furthermore, a comparative analysis of state-of-the-art models to benchmark has been concluded to proposed architecture’s performance against established methods. Overall, this research contributes to the advancement of image classification methodologies on the CIFAR-10 dataset, with potential applications in various real-world domains, such as object recognition and autonomous systems.

Published Paper   E-Certificate

Akshay Akhileshwaran (October-2023). Veritas AI: CIFAR-10 Image Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(10), c50-c54. https://ijnrd.org/papers/IJNRD2310207.pdf

Volume 8 Issue 10, October-2023

Pages : c50-c54

Paper Reg. ID: IJNRD_205256

Published Paper Id: IJNRD2310207

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Research Area: Engineering

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Published Paper PDF: https://ijnrd.org/papers/IJNRD2310207.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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