基于ResNet和注意力机制的花卉识别

花卉识别在生活中有重要的应用价值，传统的花卉识别方法存在识别准确率低、泛化能力较弱等问题。针对这些问题，本文提出一种加入注意力机制的ResNet34网络模型，在ResNet34第一层卷积层和各残差块后加入通道注意力机制、空间注意力机制，并使用迁移学习训练网络模型。实验表明，在花卉数据集上ResNet34比AlexNet、VGG-16、GoogLeNet识别准确率更高，加入注意力机制并使用迁移学习的ResNet34模型的识别准确率比原模型提高了6.1个百分点，比仅使用迁移学习的原模型提高了1.1个百分点。与传统深度学习模型相比，本文提出的模型显著地提高了识别准确率。

Flower Recognition Based on ResNet and Attention Mechanism

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.