【摘要】 针对传统的蘑菇特征提取方法分类效率低且效果差的问题，提出了一种轻量型的蘑菇图像分类模型。由于实验所用数据集较小，所提分类模型在基于Imagenet数据集的迁移学习中初始化EfficientNetV2模型并修改全连接层。同时为了减少网络中参数影响，对原EfficientNetV2模型进行精简，去除了网络中重复的模块。最后用特征提取效果更好的coordinate attention(CA)注意力机制替代原来MBConv模块中的squeeze-and-excitation机制，得到了新的CA-EfficientNetV2。实验结果表明：所提EfficientNetV2与经典ResNet50模型和RegNet相比分类准确率分别提高了10个百分点和2个百分点左右，并得到较高的泛化性能；相较于原始EfficientNetV2，分类准确率提高了3个百分点。即CA-EfficientNetV2在蘑菇分类问题上具有更高的准确率，具有较高的分类性能。更多还原

【Abstract】 In view of the low efficiency and poor effect of the traditional mushroom feature extraction method, a lightweight mushroom image classification model is proposed. In view of the small dataset used in the experiment, this classification model initializes the EfficientNetV2 model and modifies the full connection layer in the migration study based on the Imagenet dataset. At the same time, in order to reduce the parameter influence in the network, the original EfficientNetV2 model is streamlined to remove duplicate modules in the network. Finally, the squeeze-and-excitation mechanism in the original MBConv module is replaced with the coordinate attention(CA) attention mechanism with better feature extraction effect, and the new CA-EfficientNetV2 network is obtained. The experimental results show that compared with the classical ResNet50model and RegNet, the classification accuracy of the proposed EfficientNetV2 is improved by about 10 percentage points and2 percentage points respectively, and higher generalization performance is obtained; compared with the original EfficientNetV2, the classification accuracy is improved by 3 percentage points. That is, CA-EfficientNetV2 has higher accuracy and classification performance in mushroom classification.更多还原