关键词: 深度学习, ResNet34, 注意力机制, 迁移学习, 花卉识别

Abstract: Flower recognition has important application value in life, and the traditional flower recognition methods have some problems, such as low recognition accuracy and weak generalization ability. To solve these problems, this paper proposes a ResNet34 network model with attention mechanism. After the first convolutional layer and each residual block of ResNet34, channel attention mechanism and spatial attention mechanism are added, and the transfer learning is used for training network model. Experiment shows that ResNet34 has a higher recognition accuracy rate than AlexNet, VGG-16 and GoogLeNet on the flower data set. The ResNet34 model with attention mechanism and transfer learning has 6.1 percentage points higher recognition accuracy than the original model, and 1.1 percentage points higher recognition accuracy than the original model with transfer learning only. Compared with traditional deep learning models, the model proposed in this paper significantly improves the recognition accuracy.

Key words: deep learning, ResNet34, attention mechanism, transfer learning, flower recognition