The widespread adoption of deep learning models in diverse applications often clashes with the stringent resource constraints of deployment environments, particularly for mobile and edge devices. Traditional deep neural networks, while achieving remarkable accuracy, typically demand significant computational power, memory, and energy. EfficientNet architectures introduced a systematic compound scaling method to efficiently scale model width, depth, and resolution, yielding superior performance across various model…