Fig. 2: SLEAP is fast, efficient and accurate.
From: SLEAP: A deep learning system for multi-animal pose tracking
a, Speed versus accuracy of different animal pose-estimation frameworks on a single-animal dataset6. Points correspond to sampled measurements of batch-processing speed over 1,280 images with the highest-accuracy model replicate from each framework. Accuracy was evaluated on a held-out test set of 150 images. b, Speed versus batch size for multi-animal datasets. Points correspond to sampled measurements of batch-processing speed over 1,280 images and five replicates. OF, open field. c, Sample efficiency across multi-animal datasets. Points indicate accuracy of model training replicates on the held-out test set. d–g, Body part-wise landmark-localization accuracy. Circles denote the 95th percentile of localization errors, and histograms correspond to full error distribution evaluated on held-out test sets (n = 150 frames for flies, n = 100 frames for mice). L, left; R, right; hi, hind; fr, front.
