# ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes

Source: [ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes](<https://www.sota2.com/research/paper/68fa6c811e209131cea34624>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T14:33:15.733Z

Authors: Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu, Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, Kate Saenko

Published: 2021-06-04T22:17:09.000Z

## Abstract

Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://ai.bu.edu/zerowaste/.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on TrashCan 1.0 5 Epochs (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-trashcan-1-0-5-epochs-test.md>): Semantic segmentation; TrashCan 1.0 5 Epochs (test); mIoU: 80.32.

- [Semantic Segmentation on ZeroWaste 5 Epochs (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-zerowaste-5-epochs-test.md>): Semantic segmentation; ZeroWaste 5 Epochs (test); mIoU: 52.13.

- [Semantic Segmentation on TACO 10 Epochs (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-taco-10-epochs-test.md>): Semantic segmentation; TACO 10 Epochs (test); mIoU: 29.19.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.02740>)

[PDF](<https://arxiv.org/pdf/2106.02740.pdf>)

[Implementation](<http://ai.bu.edu/zerowaste/>)