准确并高效地识别青铜铭文在考古学、历史学、文字学等领域具有重要的意义。构建了公开的青铜铭文数据集,为后续研究提供了数据参考。同时在基于L1范数的卷积核剪枝基础上,提出了一种自动化模型剪枝策略,无需人为设置剪枝比例即可自动完成剪枝操作。实验结果表明,以LeNet网络为基础模型,剪枝后的参数量和FLOPS仅为原模型的30%和69%,准确率达到97.62%,所提方法适用于青铜铭文数据集。

Accurate and efficient identification of bronze inscriptions is of great significance in archaeology, history, philology and other fields. We construct the publicly available bronze inscription data set to provide data reference for subsequent research. At the same time, on the basis of the convolution kernel pruning based on L1 norm, an automatic model pruning strategy is proposed. The experimental results show that if taking the LeNet network as the basic model, the number of parameters and the FLOPS after pruning are only 30% and 69% of those for the original model, and the accuracy is up to 97.62%. The proposed method can be well adapted to the bronze inscription data set.