Lightweight CNN

Lightweight Convolutional Neural Networks (CNNs) aim to achieve high accuracy in various computer vision tasks while minimizing computational cost and model size, making them suitable for resource-constrained devices like mobile phones and embedded systems. Research currently focuses on developing novel architectures, such as variations of MobileNet, EfficientNet, and U-Net, incorporating techniques like depthwise separable convolutions, and exploring hybrid models combining CNNs with Vision Transformers. This pursuit of efficiency is significant for expanding the accessibility and applicability of deep learning to a wider range of applications, particularly in areas like medical image analysis, mobile robotics, and edge computing.