lightWeight Network

Lightweight networks are designed to achieve high performance in various computer vision and machine learning tasks while minimizing computational cost and memory footprint, making them suitable for resource-constrained environments like mobile devices and embedded systems. Current research focuses on developing efficient architectures, such as CNN-Transformer hybrids and novel lightweight convolutional modules, often incorporating techniques like knowledge distillation, channel pruning, and attention mechanisms to optimize model size and speed without sacrificing accuracy. This area is significant because it enables the deployment of advanced AI capabilities in applications where computational resources are limited, impacting fields ranging from medical imaging and robotics to remote sensing and mobile computing.