Transformers have shown promising results in image recognition tasks, often outperforming traditional Convolutional Neural Networks (CNNs) in terms of accuracy and efficiency. However, the choice between transformers and CNNs depends on the specific problem and the available resources. Transformers may require more computational power and memory, while CNNs can be more efficient in certain scenarios. It is essential to consider the trade-offs between accuracy, efficiency, and computational cost when choosing between transformers and CNNs for image recognition tasks.