应用卷积神经网络分类的Hindeodus牙形刺细粒度数据集

A dataset of fine-grained fossils of the conodont genus Hindeodus for classification using convolutional neural networks

随着人工智能浪潮的兴起,利用卷积神经网络对化石进行分类识别已得到越来越多的关注,并表现出了巨大的应用潜力。通过调研发现,前人所分类的化石物种基本属于不同的属、科或更高级的生物分类单位。然而,现实中对于同属异种化石的鉴定往往是重点和难点,也意味着前人所训练的分类器可能并不能很好地用于实际的化石鉴定。鉴于此,本文通过文献收集,建立了一个包含12种同属于Hindeodus属的牙形刺数据集,同时提供了对原始数据增强后的数据集。由于该数据集具有细粒度的特点,用户可以使用卷积神经网络并结合细粒度图像特征提取技术对其进行训练。针对数据集存在数据量较少、类别不均衡等不足,建议用户在训练时使用分层K折交叉验证、迁移学习和加权损失函数等手段来解决以上问题。本文数据集旨在为生物化石智能识别领域增补一个细粒度化石数据集,其可作为卷积神经网络对细粒度(种一级)化石进行智能鉴定的实验数据集。本数据集所遵循的细粒度原亦可以作为建立其他门类化石数据集的参考。

With the rise of artificial intelligence,the booming application of convolutional neural networks to the classification and identification of fossils has attracted more and more attention.According to our survey,it is found that the species classified by previous authors basically belong to different genera,families or higher biological taxonomic units.However,in fact,the identification of fossils between species within a genus is often the focus and challenge for the identification task,which means that the previously trained classifiers may not be suitable for actual fossil identification.On this basis,in this paper,we built a dataset covering 12 species of the conodont genus Hindeodus by means of literature collection,while providing an augmented dataset of the original data.Since the dataset is fine-grained,users can train it by using convolutional neural network combined with fine-grained image feature extraction technology.In view of the deficiencies of the dataset such as small amount of data and unbalanced classes,it is suggested that users use stratified K-fold cross-validation,transfer learning and weighted loss function in the training task to solve the above problems.The dataset is aimed to add a fine-grained fossil dataset to the field of intelligent identification of biological fossils,which can be used as an experimental dataset for intelligent identification of fine-grained(species-level)fossils by convolutional neural networks.The fine-grained primitive followed by this dataset can also be used as a reference for the establishment of other fossil datasets.