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

Abstract

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.