Data from: Corrigendum to: Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars

Corrigendum to "Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars". In a previous paper, we presented some convolutional neural network (CNN) models to classify images of tooth scores made by lions and jaguars through deep learning computer vision. In that work, we reached an accuracy of 82% of the testing set correctly classified. However, such an accuracy is biased, since the original sample was highly unbalanced. Therefor, now we present the results which correct the problems of the previously published models by producing more balanced classifications and also by achieving higher accuracy.

《深度学习提升埋藏学分辨率：区分狮与美洲豹齿痕的高精度方法》勘误。在先前发表的论文中，我们借助深度学习计算机视觉技术，构建了多款卷积神经网络（Convolutional Neural Network，CNN）模型，用于对狮与美洲豹所形成的齿痕图像进行分类。该研究的测试集分类准确率达到82%。然而，由于原始样本存在严重的类别不平衡问题，该准确率存在偏倚。因此，本研究针对此前发表模型的缺陷进行修正，通过构建更均衡的分类方案并实现更高的分类准确率，现呈现修正后的研究结果。