Scalability of convolutional neural networks (CNNs) is an important factor that directly affects computational efficiency and model performance in the fields of computer vision and deep learning. In this study, we introduce EnhanceNet, a unique method that challenges the traditional approaches to CNN scaling. In contrast to conventional scaling techniques, which concentrate just on expanding the model’s width, depth, or resolution, EnhanceNet presents a comprehensive viewpoint on model improvement. Our method rethinks CNN scaling methodologies by utilizing ideas from computing efficiency, feature representation, and network architecture. EnhanceNet recommends making thoughtful modifications to the model’s parameters based on the particular task needs and computing limitations rather than just changing the model’s parameters arbitrarily. EnhanceNet offers notable performance gains across a variety of computer vision applications by carefully balancing model complexity and processing cost.