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. 2023 Sep 14;13:15196. doi: 10.1038/s41598-023-41459-w

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© The Author(s) 2023

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Particle detection performance. (A) An example of TEM image with YOLOv5 bounding box on the top. (B) Training data size sensitivity with F1 score as a function of number of training images. The number of particles per image ranges from ~ 20 to 30 and the total number of particles for 10 images is 259. Inset, F1 score with different confidence value for different training image numbers (C) The bounding boxsize filter removed particles with aspect ratio > 1.5. (D) Confusion matrix. The arrow represents the results after applying the filter.