基于交叉多尺度深度残差网络的陶瓷器型分类

Classification of Ceramic Ware Types Based on Cross-Multiscale Deep Residual Networks

【目的】利用神经网络模型解决少样本陶瓷器型分类问题,通过多尺度和注意力机制优化,提高模型针对陶瓷器型分类的性能。【方法】提出一种基于坐标注意力机制和多尺度融合的瓶颈结构,并将其应用于残差网络中,引入尺度之间的关系,提升残差网络在多尺度方面的建模能力。【结果】在陶瓷器型图像公共数据集上,本文模型只需进行少样本学习即能达到95.71%的分类准确率,相比基准模型ResNet50提升了1.01个百分点。在精确率、召回率和F1分数指标上,本文模型比ResNeSt50分别提升了20.43、20.53和20.52个百分点。【局限】模型推理效率下降,不适用于需要进行快速陶瓷器型分类的场景。【结论】多尺度改进方式在陶瓷器型分类中简单有效,在处理此类任务或者相近的人文数据分类任务时,可优先考虑这种优化策略。

[Objective]A neural network model is used to solve the problem of ceramic ware types classification with few samples,and the performance of the model for ceramic ware types classification is improved by using multiscale and attention mechanism optimization.[Methods]A bottleneck structure based on coordinate attention mechanism and multiscale fusion is proposed and applied to the residual network,which innovatively introduces the relationship between scales and ultimately improves the modeling ability of the residual networks in terms of multiscale.[Results]On the public dataset of ceramic ware types images,this model achieves a classification accuracy of 95.71%with only a few samples learning,representing an improvement of 1.01 percentage points over the baseline model ResNet50.In terms of precision,recall,and F1 score metrics,the proposed model outperforms ResNeSt50 by 20.43,20.53,and 20.52 percentage points,respectively.[Limitations]Although the model’s recognition accuracy and other metrics have increased,the efficiency of inference has decreased,and it would not be suitable for scenarios where rapid ceramic ware classification is required.[Conclusions]The multiscale improvement approach is simple and effective in ceramic ware types classification,and this optimization strategy should be prioritized when performing this type of task or similar humanity data.