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The emergence of deep learning models led to the development of effective classification models for various visual recognition tasks. Vision Transformer is a recent deep learning model that accomplished state-of-the-art results in various visual recognition tasks. In this work, we propose a deep learning-based approach using Vision Transformer that uses data augmentation during testing to optimize performance. The experimental evaluation of the proposed approach on the Indian medical flower image datasets suggests its effectiveness.

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Evaluation of Vision Transformer with Augmented Data for Classification of Medical Flowers

  • 会议论文

  • 发表日期：

    2026年

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  • Earnest Paul Ijjina

摘要

The emergence of deep learning models led to the development of effective classification models for various visual recognition tasks. Vision Transformer is a recent deep learning model that accomplished state-of-the-art results in various visual recognition tasks. In this work, we propose a deep learning-based approach using Vision Transformer that uses data augmentation during testing to optimize performance. The experimental evaluation of the proposed approach on the Indian medical flower image datasets suggests its effectiveness.