Research on Application of Improved ResNet Network Based on Transfer Learning
In addressing the potential performance issues and weak generalization capabilities of traditional ResNet networks in image classification tasks across various research domains,this study proposes an improved ResNet model,TA-ResNet50,based on transfer learning.The model incorporates channel and spatial attention modules to enhance its focus on critical information.Optimization designs are applied to the fully connected layers,and a Dropout layer is introduced to mitigate the risk of overfitting.Transfer learning techniques are employed to accelerate model training and improve generalization.To validate the effectiveness of the TA-ResNet50 model,a series of experiments are conducted on the GTSRB,CIFAR-10,and CIFAR-100 datasets.Experimental results demonstrate that compared to traditional ResNet models,TA-ResNet50 achieves respective increases of 12.73%,12.51%,and 10.98%in image recognition accuracy on these datasets.Additionally,the TA-ResNet50 model exhibits enhanced robustness and generalization capabilities.This study provides valuable insights for the application of transfer learning in deep neural networks.
transfer learningResNet50attention mechanismimage classification
万方数据
维普
