CNR-IOM Istituto Officina dei Materiali, c/o SISSA, via Bonomea 265, 34136 Trieste, Italy.
Institute for Manufacturing, Department of Engineering, University of Cambridge, 17 Charles Babbage Road, Cambridge CB3 0FS, UK.
Sci Data. 2018 Aug 28;5:180172. doi: 10.1038/sdata.2018.172.
In this paper, we present the first publicly available human-annotated dataset of images obtained by the Scanning Electron Microscopy (SEM). A total of roughly 26,000 SEM images at the nanoscale are classified into 10 categories to form 4 labeled training sets, suited for image recognition tasks. The selected categories span the range of 0D objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces as well as patterned surfaces, and 3D structures such as microelectromechanical system (MEMS) devices and pillars. Additional categories such as tips and biological are also included to expand the spectrum of possible images. A preliminary degree of hierarchy is introduced, by creating a subtree structure for the categories and populating them with the available images, wherever possible.
在本文中，我们展示了第一个公开的、由扫描电子显微镜（SEM）获得的人工标注图像数据集。总共约 26000 张纳米级 SEM 图像被分为 10 个类别，形成了 4 个标记训练集，适合图像识别任务。所选类别涵盖了从 0D 物体（如颗粒）、1D 纳米线和纤维、2D 薄膜和涂层表面以及图案化表面，到 3D 结构（如微机电系统（MEMS）器件和立柱）等范围。还包括了其他类别，如尖端和生物，以扩展可能的图像范围。通过为类别创建子树结构，并在可能的情况下用可用图像填充它们，引入了初步的层次结构。
