Data and materials sharing site for Convolutional neural net face recognition works in non-human-like ways Abstract Convolutional neural networks (CNNs) give state of the art performance in many pattern recognition problems but can be fooled by carefully crafted patterns of noise. We report that CNN face recognition systems also make surprising ‘errors’. We tested six commercial face recognition CNNs and found that they outperform typical human participants on standard face matching tasks. However, they also declare matches that humans would not, where one image from the pair has been transformed to appear a different sex or race. This is not due to poor performance; the best CNNs perform almost perfectly on the human face matching tasks, but also declare the most matches for faces of a different apparent race or sex. Although differing on the salience of sex and race, humans and computer systems are not working in completely different ways. They tend to find the same pairs of images difficult, suggesting some agreement about the underlying similarity space. Data The summary data file gives the CNN responses and average human response to each of the four test sets. Kent Face Matching task https://www.kent.ac.uk/school-of-psychology/kentfacematch/index.html Glasgow Unfamiliar Face Database, used for testing face transformations. http://www.facevar.com/glasgow-unfamiliar-face-database Lisa deBruine's averages of sets of female white and black faces. https://figshare.com/articles/Young_adult_composite_faces/4055130/1 The Models, Makeup and Dutch face sets are not freely available for copyright reasons. Chris Meissner's set used for the apparent race transform of male faces: http://iilab.utep.edu/stimuli.htm