  We present the DeepScores dataset with the goal of advancing the
state-of-the-art in small objects recognition, and by placing the question of
object recognition in the context of scene understanding. DeepScores contains
high quality images of musical scores, partitioned into 300,000 sheets of
written music that contain symbols of different shapes and sizes. With close to
a hundred millions of small objects, this makes our dataset not only unique,
but also the largest public dataset. DeepScores comes with ground truth for
object classification, detection and semantic segmentation. DeepScores thus
poses a relevant challenge for computer vision in general, beyond the scope of
optical music recognition (OMR) research. We present a detailed statistical
analysis of the dataset, comparing it with other computer vision datasets like
Caltech101/256, PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, smaller computer
vision datasets, as well as with other OMR datasets. Finally, we provide
baseline performances for object classification and give pointers to future
research based on this dataset.
