The data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour.
In the following we provide some details about the datasets:

• Each development (competition) dataset consists of 1000 (2000) 'non-defective' and of 150 (300) 'defective' images saved in grayscale 8-bit PNG format.
• Each dataset is generated by a different texture model and defect model.
• 'Non-defective' images show the background texture without defects, 'defective' images have exactly one labelled defect on the background texture.
• All datasets has been randomly split into a training and testing sub-dataset of equal size.
• Weak labels are provided as ellipses roughly indicating the defective area. Technically, defective images are augmented with a separate grayscale 8-bit image in the PNG format located in a folder 'Label'. The values 0 and 255 denote background and de