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. 2022 Apr 12;19(4):496–504. doi: 10.1038/s41592-022-01443-0

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 5 — Assembly rates vs. graphs size for the four datasets. Improving the assembly robustness via calibration with labeled data in large graphs incurs no extra computational cost at best, and a slowdown by 25% at worst; remarkably, it is found to accelerate assembly speed in small graphs. Relying exclusively on keypoint identity prediction results in average speeds of around 5600 frames per second, independent of graph size. Three timing experiments were run per graph size (lighter colored dots) and averages are shown. Note that assembling rates exclude CNN processing times. Speed benchmark was run on a workstation with an Intel(R) Core(TM) i9-10900X CPU 3.70GHz.