Fine-grained Image Recognition Based on Improved EfficientNet
When the ordinary CNN model is directly applied to fine-grained image recognition,the key features are not extracted sufficiently,resulting in low recognition accuracy.In order to solve this problem,this paper proposes a fine-grained image recognition algorithm based on improved EfficientNet,with EffcientNetB3 as the backbone,and adds a CBAM attention module before the Global Average Pooling Layer to improve the key feature extraction ability of the model.In this paper,the fine-grained recognition network is trained by transfer learning,and the experimental results show that the recognition accuracy of the improved model on the CUB-200-2011 dataset reaches about 84.5%,which is 5.4%higher than that of the original network,and the model complexity is lower and the recognition accuracy is better than that of the commonly used CNN model.
国家科技期刊平台
NSTL
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