Figure 3.

Model training and evaluation data flow. The crowd dataset consisted of naturalistic, highly diverse data divided by dog into five folds. The eat/drink dataset focused on high-quality eating and drinking data. Behavior classification models were trained and evaluated in a cross-validated fashion (where a given model i is trained on all folds of data except fold i) on the crowd dataset, and the first of these five models was also evaluated on the eat/drink dataset. Confusion matrices and classification metrics were produced for each dataset using the resulting predictions.