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

TL;DR

DeepScores is a large, high-quality dataset of musical scores with diverse small objects, designed to advance small object recognition and scene understanding in computer vision.

Contribution

The paper introduces DeepScores, the largest public dataset for small object recognition, with comprehensive annotations and baseline results, fostering progress in computer vision and optical music recognition.

Findings

DeepScores contains nearly 100 million small objects.

Baseline models show promising results on classification tasks.

The dataset outperforms existing datasets in size and diversity.

Abstract

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.