Extended Data Fig. 6. SLEAP UNet versus DeepLabCut ResNet performance for multi-animal pose estimation.

a, Relative accuracy as a function of training time for flies and mice (OF) datasets. Accuracy evaluated on a held-out test set by using model checkpoints saved at every epoch (checkpointing time not included). Accuracy is normalized to the maximum accuracy (mAP) achieved over all epochs. b, Summary of training efficiency across different model types and datasets. Time is the minimum training time from (a) required to reach 90% peak accuracy. c, Speed versus accuracy trade-off of using SLEAP UNet versus DLC ResNet models for multi-instance pose estimation. Points denote benchmark replicates and lines connect means per condition. DLC ResNet in all panels refers to an implementation of a ResNet50-based architecture configured to mimic the default configuration in DeepLabCut.