Data from: 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. | Programming language: Python. The architectures were trained on 70% of the original image set.