Classification of traditional textile patterns of Xinjiang nomads based on ResNet 18 model
To address the issues of low accuracy and slow speed in traditional methods for pattern recognition and classification of textile patterns among nomadic ethnic groups in Xinjiang,an improved convolutional neural network(ResNet 18-CA)for the recognition of textile patterns among Xinjiang nomadic ethnic groups was proposed in this paper,which enhances the extraction of pattern features by adding an attention mechanism module to the ResNet 18 convolutional neural network model.Additionally,the concept of transfer learning to effectively prevent overfitting of the network was introduced in the paper.The improved convolutional neural network model has a classification recognition accuracy of 98.62%on the established Xinjiang traditional textile pattern data set,which is 3.72%higher than the original ResNet 18 model,but the model is only 0.2 MB larger,and the improved neural network model has a higher classification accuracy.
万方数据
维普
