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Notebook","category":"Industrial Ecology","sub_category":"Circular Economy and Waste","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/wiki/pedropro/TACO/images/logonav.png\" width=\"25%\"/\u003e\n\u003c/p\u003e\n\nTACO is a growing image dataset of waste in the wild. It contains images of litter taken under\ndiverse environments: woods, roads and beaches. These images are manually labeled and segmented\naccording to a hierarchical taxonomy to train and evaluate object detection algorithms. Currently,\nimages are hosted on Flickr and we have a server that is collecting more images and\nannotations @ [tacodataset.org](http://tacodataset.org)\n\n\n\u003cdiv align=\"center\"\u003e\n  \u003cdiv class=\"column\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/wiki/pedropro/TACO/images/1.png\" width=\"17%\" hspace=\"3\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/wiki/pedropro/TACO/images/2.png\" width=\"17%\" hspace=\"3\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/wiki/pedropro/TACO/images/3.png\" width=\"17%\" hspace=\"3\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/wiki/pedropro/TACO/images/4.png\" width=\"17%\" hspace=\"3\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/wiki/pedropro/TACO/images/5.png\" width=\"17%\" hspace=\"3\"\u003e\n  \u003c/div\u003e\n\u003c/div\u003e\n\u003c/br\u003e\n\nFor convenience, annotations are provided in COCO format. Check the metadata here:\nhttp://cocodataset.org/#format-data\n\nTACO is still relatively small, but it is growing. Stay tuned!\n\n# Publications\n\nFor more details check our paper: https://arxiv.org/abs/2003.06975\n\nIf you use this dataset and API in a publication, please cite us using: \u0026nbsp;\n```\n@article{taco2020,\n    title={TACO: Trash Annotations in Context for Litter Detection},\n    author={Pedro F Proença and Pedro Simões},\n    journal={arXiv preprint arXiv:2003.06975},\n    year={2020}\n}\n```\n\n# News\n**December 20, 2019** - Added more 785 images and 2642 litter segmentations. \u003cbr/\u003e\n**November 20, 2019** - TACO is officially open for new annotations: http://tacodataset.org/annotate\n\n# Getting started\n\n### Requirements \n\nTo install the required python packages simply type\n```\npip3 install -r requirements.txt\n```\nAdditionaly, to use ``demo.pynb``, you will also need [coco python api](https://github.com/cocodataset/cocoapi). You can get this using\n```\npip3 install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI\n```\n\n### Download\n\nTo download the dataset images simply issue\n```\npython3 download.py\n```\nAlternatively, download from [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3587843.svg)](https://doi.org/10.5281/zenodo.3587843)\n\nOur API contains a jupyter notebook ``demo.pynb`` to inspect the dataset and visualize annotations.\n\n**Unlabeled data**\n\nA list of URLs for both unlabeled and labeled images is now also provided in `data/all_image_urls.csv`.\nEach image contains one URL for each original image (second column) and one URL for a VGA-resized version (first column)\nfor images hosted by Flickr. If you decide to annotate these images using other tools, please make them public and contact us so we can keep track.\n\n**Unofficial data**\n\nAnnotations submitted via our website are added weekly to `data/annotations_unofficial.json`. These have not yet been been reviewed by us -- some may be inaccurate or have poor segmentations. \nYou can use the same command to download the respective images:\n```\npython3 download.py --dataset_path ./data/annotations_unofficial.json\n```\n\n### Trash Detection\n\nThe implementation of [Mask R-CNN by Matterport](https://github.com/matterport/Mask_RCNN)  is included in ``/detector``\nwith a few modifications. Requirements are the same. Before using this, the dataset needs to be split. You can either donwload our [weights and splits](https://github.com/pedropro/TACO/releases/tag/1.0) or generate these from scratch using the `split_dataset.py` script to generate \nN random train, val, test subsets. For example, run this inside the directory `detector`:\n```\npython3 split_dataset.py --dataset_dir ../data\n```\n\nFor further usage instructions, check ``detector/detector.py``.\n\nAs you can see [here](http://tacodataset.org/stats), 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.\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/wiki/pedropro/TACO/images/teaser.gif\" width=\"75%\"/\u003e\u003c/p\u003e\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.3587843"],"works":{"https://doi.org/10.5281/zenodo.3587843":null},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/528","html_url":"https://ost.ecosyste.ms/projects/528"}