README.md

metadata

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/*

View on Pictograph · Pictograph Research · Creative Commons Attribution 4.0

About

TACO is a computer-vision dataset curated and annotated on Pictograph. 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:

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.

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):

{
  "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. When you use this data, please credit Proenca and Simoes (2020).

Source and attribution

This dataset is derived from TACO, 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

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

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Published from Pictograph - annotate, train, and deploy from one API.