|Description||Dataset of 3139 screenshots used during the development of the Appsthetics neural network model for the automatic assessment of Android user interfaces.|
|Data collection||This dataset is composed of images of screenshots of Android apps developed with App Inventor that are available in the App Inventor Gallery and Android interfaces of the RICO dataset. The interfaces were selected manually to avoid duplication of images and to assure ethical aspects.|
|Data formats||Data organization for classification: Images in JPG (.jpg) format with 239 × 425 pixels, grouped in 4 groups (beautiful, more or less, ugly, undecided) with respect to their visual aesthetic based on the results of a human rating process: Several online questionnaires, each with 150 random images (without repetition) and 3 voting options (beautiful, more or less ugly), were generated via Google Forms. Each questionnaire was answered by three different volunteers. Images were grouped based on the agreement of at least 2 of the 3 votes. If there was no agreement on the visual aesthetics, the image was included in the ‘undecided’ category.
Data organization for regression: JPG (.jpg) images with 239 × 408 pixels, all grouped into a single set, together with a CSV file (.csv) containing, per line, the image name, a comma, the image score, a comma, and a boolean value indicating whether the image is part of the test suite or not. The visual aesthetics scores were calculated based on the same survey mentioned above, assigning numerical values to the response alternatives: beautiful = 1.0, more or less = 0.5, ugly = 0.0. After conversion, an average was calculated from the total obtained. In this case, only images with total disagreement with the votes were disregarded.
|Download||Full dataset, organized into two main folders: classification and regression
Git with the code and Jupyter notebook of our experiments
|Licence||Files available on the AppInventor Gallery are licensed under CreativeCommons 4.0. The RICO dataset does not provide any information on licensing.
Our research is available under creative commons Attribution-NonCommercial-ShareAlike 4.0 International
|Citation||Heiderscheidt Martins, O., Gresse von Wangenheim, C. and von Wangenheim, A. (2019). User Interfaces Dataset for the Automated Assessment of Visual Aesthetics of Mobile User Interfaces with Deep Learning, GQS/INCoD/INE/UFSC [Data set].|
|Keywords||Aesthetics, Mobile application, Android, Deep learning, Visual design|