Research on fine-grained bird image classification based on feature fusion
Feature pyramid network(FPN)is widely used for small object detection and localization,owing to its ability to fuse features from different scales to provide rich semantic information for each feature level.However,the current FPN still cannot build connections between features across scales,and has suboptimal classification accuracy.To address this,the feature fusion pyramid network(FFPN)is proposed,which effectively improves the performance of fine-grained bird image classification by incorporating FFPN modules into the ResNet50 backbone.The model achieves 83.379%classification accuracy on CUB-200-2011 dataset and 91.201%on Bird-400 dataset,realizing good classification results.
NETL
NSTL
万方数据
维普
