SDPL demonstrates competitive performance compared to other state-of-the-art methods in terms of accuracy and robustness. However, when considering computational efficiency, SDPL may require more resources due to its dense partition strategy and shifting-fusion strategy. These additional processing steps can increase the computational load and time required for inference compared to simpler models that do not incorporate such complex strategies.