LSNet is a new family of lightweight vision models that leverage a "See Large, Focus Small" strategy, inspired by the human visual system, to improve efficiency and performance in various vision tasks. Utilizing LS convolution, which combines large-kernel perception with small-kernel aggregation, LSNet outperforms existing lightweight networks while maintaining computational efficiency. The models have been trained on ImageNet-1K and tested on a Nvidia RTX3090 for throughput.