TY - DATA
T1 - Training and test data for the preparation of the article: Convolutional Neural Network Applied for Nanoparticle Classification using Coherent Scaterometry Data
PY - 2020/05/29
AU - Dmytro Kolenov
AU - D. (Davy) Davidse
UR - https://data.4tu.nl/articles/dataset/Training_and_test_data_for_the_preparation_of_the_article_Convolutional_Neural_Network_Applied_for_Nanoparticle_Classification_using_Coherent_Scaterometry_Data/12694175/1
DO - 10.4121/uuid:516ab2fa-4c47-42f8-b614-5e283889b218
KW - Scattered maps
KW - dissertation
KW - maps of nanoparticles
KW - scatterometry images
N2 - 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.
ER -