Vision Transformer-Based Approach: A Novel Method for Object Recognition

This paper proposes a hybrid method to improve object recognition applications on inefficient and imbalanced datasets. The proposed method aims to enhance object recognition performance using the Vision Transformer (ViT) deep learning model and various classical machine learning classifiers (LightGBM, AdaBoost, ExtraTrees, and Logistic Regression). The Caltech-101 dataset used in the study is a low-resolution and noisy image dataset with class imbalance problems. Our method achieves better results by combining the feature extraction capabilities of the Vision Transformer model and the robust classification performance of classical machine learning classifiers. Experiments conducted on the Caltech-101 dataset demonstrate that the proposed method achieves a precision of 92.3%, a recall of 89.7%, and an accuracy of 95.5%, highlighting its effectiveness in addressing the challenges of object recognition in imbalanced datasets.