Extended Data Fig. 3: Performance of various DeepLabCut network architectures.
From: Multi-animal pose estimation, identification and tracking with DeepLabCut
(a) Overall keypoint prediction errors of ResNets-50 and the EfficientNets backbones (B0/B7), DLCRNet at stride 4 and 8. Distribution of train and test errors are displayed as light and dark box plots, respectively. Box plots show median, first and third quartiles, with whiskers extending past the low and high quartiles to ± 1.5 times the interquartile range. All models were trained for 60k iterations. n=independent image samples as follows: for train∣test per dataset: 112∣49 (tri-mouse); 379∣163 (pups); 5316∣2278 (marmosets); 70∣30 (fish). (b): Images on held-out test data, where plus indicates human ground truth, and the circle indicates the model prediction (shown for ResNet50 with stride 8). (c): Marmoset identification train-test accuracy for various backbones.
