Comparison between ResNet and EfficientNet for Image Classification – An Analytical Study of Performance and Efficiency

Abstract

Image classification is a fundamental task in computer vision, enabling applications such as medical diagnosis, autonomous driving, and facial recognition. Convolutional Neural Networks (CNNs) have driven major progress in this domain, with ResNet and Efficient Net emerging as two of the most influential architectures. ResNet introduced residual connections to overcome the degradation problem in very deep networks, while EfficientNet proposed a compound scaling strategy to jointly optimize network depth, width, and resolution. This paper presents an analytical comparison between ResNet and EfficientNet for image classification, focusing on key performance indicators, including classification accuracy, computational complexity, training efficiency, inference speed, and scalability. By synthesizing results from benchmark datasets and prior studies, the analysis highlights the trade-offs between robustness and efficiency. The findings show that ResNet remains a strong baseline with stable performance across various image classification tasks, whereas Efficient Net achieves higher accuracy-to-computation ratios, making it particularly effective in resource-constrained environments. The paper concludes with insights into the practical implications of choosing between these models for real-world image classification applications.