TACO is a growing image dataset of waste in the wild. It contains images of litter taken under diverse environments: woods, roads and beaches. These images are manually labeled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms. Currently, images are hosted on Flickr and we have a server that is collecting more images and annotations @ tacodataset.org

If you use this dataset and API in a publication, please cite us:  

@misc{Taco19,
  author       = {Pedro F. Proença and Pedro Simões},
  title        = {TACO: Trash Annotations in Context Dataset},
  year         = 2019,
  doi          = {10.5281/zenodo.3242156},
  url          = {http://tacodataset.org}
}

For convenience, annotations are provided in COCO format. Check the metadata here: http://cocodataset.org/#format-data

TACO is still relatively small, but it is growing. Stay tuned!

News

November 20, 2019 - TACO is officially open for new annotations: http://tacodataset.org/annotate

Getting started

Requirements

To install the required python packages simply type

pip3 install -r requirements.txt

Additionaly, to use demo.pynb, you will also need coco python api. You can get this using

pip3 install git+https://github.com/philferriere/cocoapi.git

Download

To download the dataset images simply issue

python3 download.py

Our API contains a jupyter notebook demo.pynb to inspect the dataset and visualize annotations.

Trash Detection

The implementation of Mask-RCNN by Matterport is included in /detector with a few modifications. Requirements are the same. Before using this, first use the split_dataset.py script to generate N random train, val, test subsets. For example, run this inside the directory detector:

python3 split_dataset.py --dataset_dir ../data

For further usage instructions, check detector/detector.py.

As you can see here, most of the original classes of TACO have very few annotations, therefore these must be either left out or merged together. Depending on the problem, detector/taco_config contains several class maps to target classes, which maintain the most dominant classes, e.g., Can, Bottles and Plastic bags. Feel free to make your own classes.