Dmytro Kolenov; D. (Davy) Davidse (2020): Training and test data for the preparation of the article: Convolutional Neural Network Applied for Nanoparticle Classification using Coherent Scaterometry Data. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:516ab2fa-4c47-42f8-b614-5e283889b218

Dataset

• Statistics
• Optical Physics
• Other Mathematical Sciences
• Numerical and Computational Mathematics

dissertation, maps of nanoparticles, Scattered maps, scatterometry images

CC0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Dmytro Kolenov , D. (Davy) Davidse

Here we supply the training and test data as used in the prepared publication of "Convolutional Neural Network Applied for Nanoparticle Classification using Coherent Scaterometry Data" by D. Kolenov, D. Davidse, J. Le Cam, S.F. Pereira. We present the "main dataset" samples in the pixel size of both 150x150 and 100x100, and for the three "fooling datasets" the pixel size is 100x100. On average each dataset contains 1100 images with the .mat extension. The .mat extension is straightforward with MatLab, but it could also be opened in Python or MS Excel. For the "main dataset" the pixels represent the sampling points, and the magnitude of these pixels represent the em field registered as the photocurrent on the split-detector. For the three types of "fooling data" the images of a 1) noisy and 2) mirrored set are also based on the photocurrent; 3) the elephant set is based on the open-source Animal-10 data.

• 2020-05-29 first online, published, posted

4TU.Centre for Research Data

media types: application/x-matlab-data, application/zip, text/plain

Convolutional neural network applied for nanoparticle classification using coherent scatterometry data

• NWO-TTW, 14660

TU Delft, Faculty of Applied Sciences, Department of Imaging Physics