{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepscores-a-dataset-for-segmentation","title":"DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects","arxiv_id":"1804.00525","date":"2018-03-27","proceeding":null,"authors":["Lukas Tuggener","Ismail Elezi","Jürgen Schmidhuber","Marcello Pelillo","Thilo Stadelmann"],"abstract":"We present the DeepScores dataset with the goal of advancing the\nstate-of-the-art in small objects recognition, and by placing the question of\nobject recognition in the context of scene understanding. DeepScores contains\nhigh quality images of musical scores, partitioned into 300,000 sheets of\nwritten music that contain symbols of different shapes and sizes. With close to\na hundred millions of small objects, this makes our dataset not only unique,\nbut also the largest public dataset. DeepScores comes with ground truth for\nobject classification, detection and semantic segmentation. DeepScores thus\nposes a relevant challenge for computer vision in general, beyond the scope of\noptical music recognition (OMR) research. We present a detailed statistical\nanalysis of the dataset, comparing it with other computer vision datasets like\nCaltech101/256, PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, smaller computer\nvision datasets, as well as with other OMR datasets. Finally, we provide\nbaseline performances for object classification and give pointers to future\nresearch based on this dataset.","url_abs":"http://arxiv.org/abs/1804.00525v2","url_pdf":"http://arxiv.org/pdf/1804.00525v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepscores-a-dataset-for-segmentation","repo_url":"https://github.com/ErenO/segmentation-dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deepscores-a-dataset-for-segmentation","repo_url":"https://github.com/apacha/OMR-Datasets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"deepscores","name":"DeepScores","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.00525","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}