Objectives:
Legal pornographic materials are a heterogenous group of audiovisual materials that depict one or more person over the age of eighteen engaging in sexual activities. The aim of this study was to train a model that could classify given types of pornographic materials.

Methods:
Materials included in the training set (3,600 materials) and the validation set (900 materials) were manually classified and tagged by psychologists-sexologists. Then, a deep neural network was trained on the dataset. Six models based on different architectures of convolutional neural networks were included in the study (ResNet152, ResNet101, VGG19, VGG16, Squeezenet 1.1, Squeezenet 1.0). Each model was trained on the same group of photographs, and fast.ai library was used for the training process.

Results:
The final model allows for the classification of more types of pornographic materials with greater efficiency than the pilot model, and thanks to the manual labelling of individual photographs, the limitations of the classification are known.

Conclusions:
The possible applications of the model in clinical sexology and psychiatry are discussed. The application of deep neural networks in sexology seems to be particularly promising for at least two reasons. Firstly, a tool for automated detection of pornographic materials involving minors can be developed and used during criminal proceeding. Secondly, after retraining the presented model on photographs of men and women not engaging in sexual activity the model could be used to filter content that is inappropriate for minors.