Lightweight Model

Lightweight models in deep learning aim to achieve high accuracy with minimal computational resources, making them suitable for deployment on resource-constrained devices like mobile phones and embedded systems. Current research focuses on developing efficient architectures, such as variations of UNet, YOLO, and transformers, often incorporating techniques like depthwise separable convolutions, knowledge distillation, and model pruning to reduce model size and computational cost while maintaining performance. This research is significant because it expands the applicability of deep learning to a wider range of applications and devices, impacting fields from medical image analysis and autonomous driving to natural language processing and resource-limited environments.