{"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/mvtec-d2s-densely-segmented-supermarket","title":"MVTec D2S: Densely Segmented Supermarket Dataset","arxiv_id":"1804.08292","date":"2018-04-23","proceeding":"ECCV 2018 9","authors":["Patrick Follmann","Tobias Böttger","Philipp Härtinger","Rebecca König","Markus Ulrich"],"abstract":"We introduce the Densely Segmented Supermarket (D2S) dataset, a novel\nbenchmark for instance-aware semantic segmentation in an industrial domain. It\ncontains 21,000 high-resolution images with pixel-wise labels of all object\ninstances. The objects comprise groceries and everyday products from 60\ncategories. The benchmark is designed such that it resembles the real-world\nsetting of an automatic checkout, inventory, or warehouse system. The training\nimages only contain objects of a single class on a homogeneous background,\nwhile the validation and test sets are much more complex and diverse. To\nfurther benchmark the robustness of instance segmentation methods, the scenes\nare acquired with different lightings, rotations, and backgrounds. We ensure\nthat there are no ambiguities in the labels and that every instance is labeled\ncomprehensively. The annotations are pixel-precise and allow using crops of\nsingle instances for articial data augmentation. The dataset covers several\nchallenges highly relevant in the field, such as a limited amount of training\ndata and a high diversity in the test and validation sets. The evaluation of\nstate-of-the-art object detection and instance segmentation methods on D2S\nreveals significant room for improvement.","url_abs":"http://arxiv.org/abs/1804.08292v2","url_pdf":"http://arxiv.org/pdf/1804.08292v2.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":[],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"mvtec-d2s","name":"MVTec D2S","full_name":"MVTec Densely Segmented Supermarket"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.08292","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}