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

arXiv: https://arxiv.org/abs/1804.00525 · HTML: https://mlsanity.com/paper/1804.00525/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 13 of 13)

- [A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance](https://mlsanity.com/paper/2207.12926/) (2022)
- [DoReMi: First glance at a universal OMR dataset](https://mlsanity.com/paper/2107.07786/) (2021)
- [A Unified Representation Framework for the Evaluation of Optical Music Recognition Systems](https://mlsanity.com/paper/2312.12908/) (2023)
- [MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2506.23009/) (2025)
- [The Renaissance of Expert Systems: Optical Recognition of Printed Chinese Jianpu Musical Scores with Lyrics](https://mlsanity.com/paper/2512.14758/) (2025)
- [Proceedings of the 4th International Workshop on Reading Music Systems](https://mlsanity.com/paper/2211.13285/) (2022)
- [Proceedings of the 3rd International Workshop on Reading Music Systems](https://mlsanity.com/paper/2212.00378/) (2022)
- [Proceedings of the 2nd International Workshop on Reading Music Systems](https://mlsanity.com/paper/2212.00380/) (2022)
- [Proceedings of the 1st International Workshop on Reading Music Systems](https://mlsanity.com/paper/2301.10062/) (2022)
- [VisionScores -- A system-segmented image score dataset for deep learning tasks](https://mlsanity.com/paper/2506.23030/) (2025)
- [From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR](https://mlsanity.com/paper/2604.20522/) (2026)
- [A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation](https://mlsanity.com/paper/2605.18436/) (2026)
- [DEMUN: Fast and accurate discovery of music notation in very large collections](https://mlsanity.com/paper/2606.31956/) (2026)

## Abstract (arXiv)

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.