In Scanning Electron Microscopy, some information coming from the instrument (Beamtime, Gun Vacuum, EHT, and so on) are automatically built into the images, but information about the target material is missing. NFFA online SEM classifier automatically classify and tag your nano-images.

Tagging the content of SEM images in a uniform way aims to produce Findable, Accessible, Interoperable and Retrievable data (see FAIR principles).

Different neural network architectures were trained and tested on a dataset of human-labelled SEM images. The best model achieved was used as the engine of the online analysis service we developed to automatically classify newly incoming images. If you are not satisfied with the result, you can manually insert a new category.

Upload your SEM images and help us improving the performance of our neural network.

PUBLICATIONS

R. Aversa, M.H. Modarres, S. Cozzini et al. The first annotated set of scanning electron microscopy images for nanoscience. Sci. Data, 5, 180172, (2018)
https://doi.org/10.1038/sdata.2018.172

M. Hadi Modarres, R. Aversa, S. Cozzini, et al. Neural Network for Nanoscience Scanning Electron Microscope Image Recognition. Scientific Reports, 7, 13282, (2017)
https://doi.org/10.1038/s41598-017-13565-z