DeepScores-A Dataset for Segmentation, Detection and Classification of Tiny Objects

Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations


We present the DeepScores dataset with the goal of advancing the state-of-the-art in small object recognition 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 million 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, and optical music recognition (OMR) research in particular. We present a detailed statistical analysis of the dataset, comparing it with other computer vision datasets like PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, as well as with other OMR datasets. Finally, we provide baseline performances for object classification, intuition for the inherent difficulty that DeepScores poses to state-of-the-art object detectors like YOLO or R-CNN, and give pointers to future research based on this dataset.
Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538637883
StatePublished - Nov 26 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


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