Decoding Visual Complexity: Object Recognition through Vision Transformers and Hybrid Architectures

In this paper, we investigate the use of sophisticated neural network architectures to improve object recognition performance on the CIFAR-10 dataset, which contains 60,000 color images divided into ten categories. We used and assessed three different models: ResNet50, Vision Transformers (ViTs), and EfficientNet. The methodology included obtaining and preprocessing the CIFAR-10 dataset, translating image labels to numerical format for machine learning compatibility, then training a simple neural network and the more complicated ResNet50 model with TensorFlow’s Keras package. In addition, we combined Vision Transformers and EfficientNet into a hybrid architecture to take advantage of their distinct characteristics. Vision Transformers were used for their worldwide attention skills, while EfficientNet was chosen for its efficiency and effectiveness in dealing with high-resolution data. We also looked into ensemble learning and feature fusion approaches to combine the advantages of each architecture. The performance of each model was measured using key classification measures such as AUC, Classification Accuracy (CA), F1 Score, Precision, and Recall. The study emphasizes the comparative merits of each model and shows how combining diverse architectures can result in considerable increases in classification accuracy. The results highlight the advantages of applying cutting-edge models and novel integration methodologies to improve image classification performance. This study offers important insights into optimizing neural network topologies for complicated object recognition problems.