Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks

The rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception ResNet v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.化石的快速精准分类鉴定在古生物学、生物地层学等领域具有重要意义。然而，分类鉴定工作往往劳动强度大且枯燥乏味，且掌握某一类群的大量前置专业知识还需要长期的训练积累。此外，不同研究者与研究团队间的鉴定结果往往存在不一致性。 据此，本研究采用深度学习辅助化石分类鉴定工作。我们通过网络爬虫从互联网采集构建了化石图像数据集（Fossil Image Dataset, FID），共获取隶属于50个化石演化支的415339张图像。随后，我们在高性能工作站上训练了三款高性能卷积神经网络（Convolutional Neural Network, CNN）。当采用迁移学习时，Inception ResNet v2架构在测试集上的平均准确率达到了0.90。微化石演化支与脊椎动物化石演化支的鉴定准确率最高，分别达到0.95与0.90。与之相对，海绵、苔藓动物以及形态多样或数据集内样本量较少的遗迹化石所属演化支的模型性能均低于0.80。可视化解释方法进一步凸显了不同化石演化支间的识别差异，并表明机器学习分类器的鉴定结果与分类学家的鉴定结果存在较高相似性。 从文献、暗数据（dark data）数字化、公民科学数据以及互联网公开数据等多渠道采集大型古生物数据集，可进一步优化深度学习方法并提升其应用普及度。此类进展未来或可推动基于图像的系统分类学为机器辅助分类所取代。相关开创性研究可涵盖微化石与部分无脊椎动物化石。为推动该领域发展，我们将训练好的模型部署于服务器，开放至www.ai-fossil.com供公众访问。