Code and notebooks for running tabular (XGBoost, LightGBM, Random Forest) and image-based (ResNet-50, VGG-16, custom CNN) classifiers for specimen identification. Designed for usability in pathology/ecology labs. R notebooks cover the tabular pipeline; Python notebooks cover the image models. See Code/ for environment requirements.