# TACO: Trash Annotations in Context for Litter Detection

arXiv: https://arxiv.org/abs/2003.06975 · HTML: https://mlsanity.com/paper/2003.06975/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 30 of 38)

- [ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes](https://mlsanity.com/paper/2106.02740/) (2021)
- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts](https://mlsanity.com/paper/2303.14152/) (2023)
- [TRansPose: Large-Scale Multispectral Dataset for Transparent Object](https://mlsanity.com/paper/2307.05016/) (2023)
- [Waste detection in Pomerania: non-profit project for detecting waste in environment](https://mlsanity.com/paper/2105.06808/) (2021)
- [SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation](https://mlsanity.com/paper/2403.18033/) (2024)
- [PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects](https://mlsanity.com/paper/2409.12659/) (2024)
- [Data augmentation and explainability for bias discovery and mitigation in deep learning](https://mlsanity.com/paper/2308.09464/) (2023)
- [A survey on bias in machine learning research](https://mlsanity.com/paper/2308.11254/) (2023)
- [GlobalWasteData: A Large-Scale, Integrated Dataset for Robust Waste Classification and Environmental Monitoring](https://mlsanity.com/paper/2602.07463/) (2026)
- [Resource Constrained Semantic Segmentation for Waste Sorting](https://mlsanity.com/paper/2310.19407/) (2023)
- [pLitterStreet: Street Level Plastic Litter Detection and Mapping](https://mlsanity.com/paper/2401.14719/) (2024)
- [Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting](https://mlsanity.com/paper/2502.01455/) (2025)
- [Learning Using Privileged Information for Litter Detection](https://mlsanity.com/paper/2508.04124/) (2025)
- [StreetView-Waste: A Multi-Task Dataset for Urban Waste Management](https://mlsanity.com/paper/2511.16440/) (2025)
- [Real-time Street Human Motion Capture](https://mlsanity.com/paper/2112.11543/) (2021)
- [Searching for Uncollected Litter with Computer Vision](https://mlsanity.com/paper/2211.14743/) (2022)
- [Garbage Segmentation and Attribute Analysis by Robotic Dogs](https://mlsanity.com/paper/2404.18112/) (2024)
- [Utilisation of Vision Systems and Digital Twin for Maintaining Cleanliness in Public Spaces](https://mlsanity.com/paper/2411.05964/) (2024)
- ["ScatSpotter" -- A Dog Poop Detection Dataset](https://mlsanity.com/paper/2412.16473/) (2024)
- [Image Segmentation and Classification of E-waste for Training Robots for Waste Segregation](https://mlsanity.com/paper/2506.07122/) (2025)
- [Robust and Label-Efficient Deep Waste Detection](https://mlsanity.com/paper/2508.18799/) (2025)
- [WS2: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting](https://mlsanity.com/paper/2509.06485/) (2025)
- [Improving Classification of Occluded Objects through Scene Context](https://mlsanity.com/paper/2510.26681/) (2025)
- [TrashDet: Iterative Neural Architecture Search for Efficient Waste Detection](https://mlsanity.com/paper/2512.20746/) (2025)
- [RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection](https://mlsanity.com/paper/2601.00398/) (2026)
- [SortWaste: A Densely Annotated Dataset for Object Detection in Industrial Waste Sorting](https://mlsanity.com/paper/2601.02299/) (2026)
- [AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal](https://mlsanity.com/paper/2601.11876/) (2026)
- [A Scoping Review of Deep Learning for Urban Visual Pollution and Proposal of a Real-Time Monitoring Framework with a Visual Pollution Index](https://mlsanity.com/paper/2602.09446/) (2026)
- [The Garbage Dataset (GD): A Multi-Class Image Benchmark for Automated Waste Segregation](https://mlsanity.com/paper/2602.10500/) (2026)

## Abstract (arXiv)

TACO is an open image dataset for litter detection and segmentation, which is growing through crowdsourcing. Firstly, this paper describes this dataset and the tools developed to support it. Secondly, we report instance segmentation performance using Mask R-CNN on the current version of TACO. Despite its small size (1500 images and 4784 annotations), our results are promising on this challenging problem. However, to achieve satisfactory trash detection in the wild for deployment, TACO still needs much more manual annotations. These can be contributed using: http://tacodataset.org/