README.md

---
license: cc-by-4.0
pretty_name: "TACO"
task_categories:
  - object-detection
  - image-segmentation
tags:
  - computer-vision
  - grass
  - sidewalk
  - bush
  - bottle
  - frisbee
  - ruins
  - pictograph
annotations_creators:
  - expert-generated
size_categories:
  - 1K<n<10K
source_datasets:
  - extended
paperswithcode_id: taco-trash-annotations-in-context
configs:
  - config_name: default
    data_files:
      - split: train
        path: "train/*"
---

[![TACO - annotated computer-vision dataset on Pictograph](https://storage.googleapis.com/pictograph-assets/hf/heroes/taco.png?v=3)](https://pictograph.io/datasets/pictograph-research/taco)

[![Open in](https://img.shields.io/badge/Open_in-Pictograph-2c5aa0)](https://pictograph.io/datasets/pictograph-research/taco) ![task](https://img.shields.io/badge/task-object_detection-1a4480) ![images](https://img.shields.io/badge/images-1,500-555) [![license](https://img.shields.io/badge/license-cc--by--4.0-3cb44b)](https://creativecommons.org/licenses/by/4.0/)

**[View on Pictograph](https://pictograph.io/datasets/pictograph-research/taco)** · [Pictograph Research](https://pictograph.io/org/pictograph-research) · [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/)

## About

**TACO** is a computer-vision dataset curated and annotated on [Pictograph](https://pictograph.io/datasets/pictograph-research/taco). The most common detected objects are grass, sidewalk, bush, bottle, frisbee, ruins. On Pictograph you can browse every annotated image, fork it into your own workspace in one click, export it in a dozen formats, or train a model on it directly.

## At a glance

| Metric | Value |
| :--- | ---: |
| Images | 1,500 |
| Annotations | 4,784 |
| Classes | 60 |
| Annotation types | bounding box, polygon |
| Splits | train |

## Quick start

Load it in one line with the `datasets` library, then read each record's boxes and class names:

```python
from datasets import load_dataset
from PIL import ImageDraw

ds = load_dataset("pictograph/taco", split="train")
example = ds[0]
image = example["image"]            # a PIL image
objects = example["objects"]        # {bbox, categories, category_names}

# draw every bounding box with its class name
draw = ImageDraw.Draw(image)
for (x, y, w, h), name in zip(objects["bbox"], objects["category_names"]):
    draw.rectangle([x, y, x + w, y + h], outline="red", width=3)
    draw.text((x, y - 12), name, fill="red")

# polygon masks live under example["segmentation"]:
#   [{"label": name, "category": idx, "points": [[x, y], ...]}, ...]
image.show()
```

Prefer a full annotation editor, one-click fork, multi-format export, and one-click training? [Open this dataset on Pictograph](https://pictograph.io/datasets/pictograph-research/taco).

## Dataset structure

This dataset uses the Hugging Face `imagefolder` layout: each split directory holds the images plus a `metadata.jsonl` that links every image to its annotations by `file_name`.

| Field | Description |
| :--- | :--- |
| `file_name` | Path to the image within the split directory. |
| `objects.bbox` | Bounding boxes as `[x, y, width, height]` (pixels). |
| `objects.categories` | Integer class index per box (matches the class list below). |
| `objects.category_names` | Human class name per box. |
| `segmentation` | List of `{label, category, points}`; `points` is a polygon ring `[[x, y], ...]`. |

## Data instance

One record (bounding boxes are `[x, y, width, height]` in pixels; the class index maps into the class list below):

```python
{
  "image": <PIL.Image (RGB)>,
  "objects": {
    "bbox": [[172.0, 192.0, 249.4, 152.7]],
    "categories": [0],
    "category_names": ["Aerosol"]
  },
  "segmentation": [
    {"label": "Aerosol", "category": 0, "points": [[176.9, 207.2], [259.0, 274.1], ...]}
  ]
}
```

## Classes

Class index matches `objects.categories` in `metadata.jsonl`.

| # | Class | Annotations |
| ---: | :--- | ---: |
| 0 | Aerosol | 10 |
| 1 | Aluminium blister pack | 6 |
| 2 | Aluminium foil | 62 |
| 3 | Battery | 2 |
| 4 | Broken glass | 138 |
| 5 | Carded blister pack | 1 |
| 6 | Cigarette | 667 |
| 7 | Clear plastic bottle | 285 |
| 8 | Corrugated carton | 64 |
| 9 | Crisp packet | 39 |
| 10 | Disposable food container | 38 |
| 11 | Disposable plastic cup | 104 |
| 12 | Drink can | 229 |
| 13 | Drink carton | 45 |
| 14 | Egg carton | 11 |
| 15 | Foam cup | 13 |
| 16 | Foam food container | 15 |
| 17 | Food Can | 34 |
| 18 | Food waste | 8 |
| 19 | Garbage bag | 31 |
| 20 | Glass bottle | 104 |
| 21 | Glass cup | 6 |
| 22 | Glass jar | 6 |
| 23 | Magazine paper | 12 |
| 24 | Meal carton | 30 |
| 25 | Metal bottle cap | 80 |
| 26 | Metal lid | 10 |
| 27 | Normal paper | 82 |
| 28 | Other carton | 93 |
| 29 | Other plastic | 273 |
| 30 | Other plastic bottle | 50 |
| 31 | Other plastic container | 6 |
| 32 | Other plastic cup | 2 |
| 33 | Other plastic wrapper | 260 |
| 34 | Paper bag | 27 |
| 35 | Paper cup | 67 |
| 36 | Paper straw | 4 |
| 37 | Pizza box | 3 |
| 38 | Plastic bottle cap | 209 |
| 39 | Plastic film | 451 |
| 40 | Plastic glooves | 4 |
| 41 | Plastic lid | 77 |
| 42 | Plastic straw | 157 |
| 43 | Plastic utensils | 37 |
| 44 | Plastified paper bag | - |
| 45 | Polypropylene bag | 3 |
| 46 | Pop tab | 99 |
| 47 | Rope & strings | 29 |
| 48 | Scrap metal | 20 |
| 49 | Shoe | 7 |
| 50 | Single-use carrier bag | 61 |
| 51 | Six pack rings | 5 |
| 52 | Spread tub | 9 |
| 53 | Squeezable tube | 7 |
| 54 | Styrofoam piece | 112 |
| 55 | Tissues | 42 |
| 56 | Toilet tube | 5 |
| 57 | Tupperware | 4 |
| 58 | Unlabeled litter | 517 |
| 59 | Wrapping paper | 12 |

## License

Released under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/). When you use this data, please credit **Proenca and Simoes (2020)**.

## Source and attribution

This dataset is derived from [TACO](http://tacodataset.org/), created by Proenca and Simoes (2020), originally licensed cc-by-4.0. We are grateful to the original authors. If you use this data, please cite the original source above.

## Citation

```bibtex
@article{proenca2020taco,
  title={TACO: Trash Annotations in Context for Litter Detection},
  author={Proenca, Pedro F. and Simoes, Pedro},
  journal={arXiv:2003.06975},
  year={2020}
}
```

---

*Published from [Pictograph](https://pictograph.io/datasets/pictograph-research/taco) - annotate, train, and deploy from one API.*