EfficientNet

EfficientNet is a landmark convolutional neural network (CNN) architecture developed by Google Research that revolutionized how models are scaled. In the 2026 AI landscape, it remains a gold standard for production-grade computer vision, particularly in environments where compute resources are constrained. Unlike traditional scaling methods that arbitrarily increase depth or width, EfficientNet utilizes a 'Compound Scaling' method that uniformly scales network depth, width, and resolution using a fixed set of scaling coefficients. This principled approach ensures that the model maintains a balance between accuracy and computational cost. The family of models, ranging from B0 to B7, allows developers to choose the optimal point on the Pareto frontier for their specific hardware. While newer Vision Transformers (ViTs) dominate ultra-large-scale datasets, EfficientNet's MBConv-based architecture is frequently preferred for real-time mobile applications, embedded systems, and industrial IoT due to its significantly lower FLOPs and memory footprint. By 2026, it is widely utilized as a backbone for more complex tasks like object detection (EfficientDet) and semantic segmentation, benefiting from a mature ecosystem of pre-trained weights on ImageNet and specialized domains like medical imaging.