# MVTec D2S: Densely Segmented Supermarket Dataset

arXiv: https://arxiv.org/abs/1804.08292 · HTML: https://mlsanity.com/paper/1804.08292/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 20 of 20)

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- [Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction](https://mlsanity.com/paper/2411.10591/) (2024)
- [Shape Distribution Matters: Shape-specific Mixture-of-Experts for Amodal Segmentation under Diverse Occlusions](https://mlsanity.com/paper/2508.01664/) (2025)
- [SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store](https://mlsanity.com/paper/2311.04645/) (2023)
- [Amodal Segmentation for Laparoscopic Surgery Video Instruments](https://mlsanity.com/paper/2408.01067/) (2024)
- [Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation](https://mlsanity.com/paper/2411.19210/) (2024)
- [R2SM: Referring and Reasoning for Selective Masks](https://mlsanity.com/paper/2506.01795/) (2025)

## Abstract (arXiv)

We introduce the Densely Segmented Supermarket (D2S) dataset, a novel benchmark for instance-aware semantic segmentation in an industrial domain. It contains 21,000 high-resolution images with pixel-wise labels of all object instances. The objects comprise groceries and everyday products from 60 categories. The benchmark is designed such that it resembles the real-world setting of an automatic checkout, inventory, or warehouse system. The training images only contain objects of a single class on a homogeneous background, while the validation and test sets are much more complex and diverse. To further benchmark the robustness of instance segmentation methods, the scenes are acquired with different lightings, rotations, and backgrounds. We ensure that there are no ambiguities in the labels and that every instance is labeled comprehensively. The annotations are pixel-precise and allow using crops of single instances for articial data augmentation. The dataset covers several challenges highly relevant in the field, such as a limited amount of training data and a high diversity in the test and validation sets. The evaluation of state-of-the-art object detection and instance segmentation methods on D2S reveals significant room for improvement.