Survey and comparison of various pre-trained CNN architectures and CNN-transformer combinations.

This study looks into a number of recently developed pre-trained models and assesses how well they perform using the ImageNet Dataset. In the disciplines of image processing, computer vision, and machine learning, image Classification is a well-known issue. In this study, we studied six different models—three transformer-based models and three convolutional neural network-based models. The findings of this research offer insightful information on the perks and drawbacks of using pre-trained models for image classification tasks. The results indicate that while transformer-based models exhibit promising outcomes, convolutional neural network-based models continue to perform better overall and with regard to accuracy. Additionally, the pre-trained models' performance may be greatly enhanced by fine-tuning them on a smaller dataset. This paper provides an assessment of recently developed pre-trained models and their performance on the ImageNet dataset, which adds to the current research in the fields of image processing, computer vision, and machine learning. The knowledge gathered from this work can guide the creation of models for image classification tasks that are more effective and accurate. [ABSTRACT FROM AUTHOR]