These folders contains all the data and trained models (including a detailed README), needed to replicate the results from the following publication: Matthia Sabatelli, Nikolay Banar, Marie Cocriamont, Eva Coudyzer, Karine Lasaracina, Walter Daelemans, Pierre Geurts & Mike Kestemont, "Advances in Digital Music Iconography. Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain". Digital Humanities Quarterly (2020). In this paper, we present MINERVA, the first benchmark dataset for the detection of musical instruments in non-photorealistic, unrestricted image collections from the realm of the visual arts. This effort is situated against the scholarly background of music iconography, an interdisciplinary field at the intersection of musicology and art history. We benchmark a number of state-of-the-art systems for image classification and object detection. Our results demonstrate the feasibility of the task but also highlight the significant challenges which this artistic material poses to computer vision. All the corresponding code, necessary for extending or replicating our work, is freely available for reuse (CC-BY) from an open code repository. This work has been generously funded by the Belgian Federal Research Agency BELSPO under the BRAIN-be program (project title: 'INSIGHT: Intelligent Neural Systems as Integrated Heritage Tools'). Project website: https://hosting.uantwerpen.be/insight/