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The topic of the talk concerns an innovative approach in the preparation of data used for training of convolutional neural networks for segmentation of color images. The problem of selecting training data and acquiring enough of them, necessary to obtain a model with the highest possible predictive accuracy, is an important issue in the topic of neural networks. Incorrect approach to it can cause that even the best prepared network architecture will not be able to generate a model that will work with satisfactory accuracy.