NFFA-EUROPE - 100% SEM Dataset

Dataset of 21,169 SEM images produced at CNR-IOM (Trieste, Italy). Images are classified into 10 categories in a folder structure. Classification labels have been checked by a group of nanoscientists on the web site http://sem-classifier.nffa.eu and only those images which have been validated by the 100% of the group have been included in the dataset.
The dataset is appropriate for the purposes of this study and in general for visual object recognition software research. Any scientific metadata associated to the measure is not present in the images. The dataset is therefore relevant as a whole, being the single images entirely detached from any specific information or scientific detail related to the displayed subject. This work has been done within the NFFA-EUROPE project (www.nffa.eu) and has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 654360 NFFA-Europe.

本数据集包含21169张扫描电子显微镜（SEM, Scanning Electron Microscope）图像，均由位于意大利的里雅斯特的CNR-IOM制作产出。所有图像按照文件夹结构被划分为10个类别。分类标签已由一组纳米科学家在网站http://sem-classifier.nffa.eu上完成审核，仅通过该小组全体成员一致验证的图像才被纳入本数据集。
本数据集既适用于本研究的相关需求，也可广泛应用于视觉目标识别软件的相关研究。图像中未附带与测量相关的任何科学元数据。因此，本数据集整体具备较高的研究价值，因为单张图像完全剥离了与所展示样本相关的所有特定信息或科学细节。
本工作依托NFFA-EUROPE项目（www.nffa.eu）开展，并获得欧盟地平线2020研究与创新计划下编号为654360的NFFA-Europe资助协议的资金支持。

搜集汇总

背景与挑战

背景概述

该数据集包含21,169张扫描电子显微镜（SEM）图像，由意大利CNR-IOM生产，并被分类为10个类别，所有图像均经过纳米科学家团队100%验证，确保分类准确性。数据集适用于视觉对象识别软件研究，但图像不包含具体科学元数据，与原始测量信息分离，使其更适合作为整体用于机器学习模型训练。此数据集由NFFA-EUROPE项目资助，源于欧盟地平线2020研究计划。