EfficientNet

EfficientNet is a family of convolutional neural networks designed for image classification and related tasks. Developed by researchers at Google AI, EfficientNet is based on a new scaling method that uniformly balances network depth, width, and resolution, enabling the models to achieve higher accuracy while using fewer parameters compared to traditional architectures. The key innovation of EfficientNet is the use of a compound scaling method, which allows for the systematic scaling of the model's dimensions, optimizing performance without drastically increasing computational requirements. This makes EfficientNet highly efficient in terms of both speed and accuracy, making it suitable for a variety of applications in computer vision, including mobile and edge devices where resources are limited. The series includes several versions, ranging from EfficientNet-B0 to EfficientNet-B7, each progressively larger and more accurate.