Lightweight Convolutional Neural Network

Lightweight convolutional neural networks (CNNs) aim to achieve high performance in image-related tasks while minimizing computational resources and memory footprint, making them suitable for deployment on resource-constrained devices like mobile phones and embedded systems. Current research focuses on developing novel architectures, such as those incorporating attention mechanisms, multi-scale feature representations, and structural reparameterization, to improve efficiency without sacrificing accuracy. These advancements are significant for various applications, including real-time object detection, medical image analysis, and mobile vision tasks, enabling broader accessibility and deployment of powerful CNN models.