Ecologists have been studying different computational models in the classification of ecological species. In this paper, we intend to take advantages of variant deep-learning models, including LeNet, AlexNet, VGG models, residual neural network, and inception models, to classify ecological datasets, such as bee wing and butterfly. Since the datasets contain relatively small data samples and unbalanced samples in each class, we apply data augmentation and transfer learning techniques. Furthermore, newly designed inception residual and inception modules are developed to enhance feature extraction and increase classification rates. As comparing against currently available deep-learning models, experimental results show that the proposed inception residual block can avoid the vanishing gradient problem and achieve a high accuracy rate of 92%.

中文翻译：

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深度学习对生态数据的分类

生态学家一直在研究生态物种分类中的不同计算模型。在本文中，我们打算利用各种深度学习模型，包括 LeNet、AlexNet、VGG 模型、残差神经网络和 inception 模型，对生态数据集进行分类，例如蜂翼和蝴蝶。由于数据集在每个类别中包含相对较小的数据样本和不平衡样本，我们应用了数据增强和迁移学习技术。此外，开发了新设计的初始残差和初始模块以增强特征提取和提高分类率。与目前可用的深度学习模型相比，实验结果表明，所提出的初始残差块可以避免梯度消失问题，并达到 92% 的高准确率。