The dataset used for the evaluation of our multitask deep multibranch neural network has been obtained from the Painter by Numbers Kaggle competition (link). However the original split is not suitable for our task. To accomplish our task we select a subset of the original dataset such that there are at least 10 images in every class for a total of 1508 artists, 125 styles and 41 genres. We call this selection the MultitaskPainting100k dataset. The dataset is split in two parts: a random 70% belonging to the train set and the remaining 30% to the test set.

Paintings from the MultitaskPaintings100k dataset. Each row contains samples from a different artist. For each artist we show paintings with different genres and styles. Color coding is used to distinguish between genres and styles.

Distributions of number of samples available for each of the 1508 artists, 41 genres and 125 styles within theMultitaskPaintings100k dataset. The names of classes are partially shown for lack of space.