TACO: Trash Annotations in Context Dataset

Instance Segmentation

TACO: Trash Annotations in Context Dataset Computer Vision Project

AI4FW

Updated a month ago

Description

TACO: Trash Annotations in Context Dataset

From: Pedro F. Proença; Pedro Simões

TACO is a growing image dataset of trash in the wild. It contains segmented images of litter taken under diverse environments: woods, roads and beaches. These images are manually labeled according to an hierarchical taxonomy to train and evaluate object detection algorithms. Annotations are provided in a similar format to COCO dataset.

The model in action:

Gif of the model running inference

Examples images from the dataset:

Example Image #2 from the Dataset Example Image #5 from the Dataset

For more details and to cite the authors:

  • Paper: https://arxiv.org/abs/2003.06975
  • Paper Citation: @article{taco2020, title={TACO: Trash Annotations in Context for Litter Detection}, author={Pedro F Proença and Pedro Simões}, journal={arXiv preprint arXiv:2003.06975}, year={2020} }
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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            taco-trash-annotations-in-context-m141d_dataset,
                            title = { TACO: Trash Annotations in Context Dataset Dataset },
                            type = { Open Source Dataset },
                            author = { AI4FW },
                            howpublished = { \url{ https://universe.roboflow.com/ai4fw-e9ig0/taco-trash-annotations-in-context-m141d } },
                            url = { https://universe.roboflow.com/ai4fw-e9ig0/taco-trash-annotations-in-context-m141d },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { nov },
                            note = { visited on 2024-12-18 },
                            }
                        
                    

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