Dataset Ninja LogoDataset Ninja:

TACO Dataset

1500602132
Tagenvironmental
Taskinstance segmentation
Release YearMade in 2019
LicenseCC BY 4.0
Download2 GB

Introduction #

Released 2019-06-10 ·Pedro F Proença, Pedro Simões

The TACO: Trash Annotations in Context is an open image dataset that focuses on waste in various real-world settings. It encompasses a collection of images depicting litter in diverse environments, ranging from tropical beaches to urban streets in places like London. The dataset is notable for its manual labeling and segmentation, providing a hierarchical taxonomy for object detection algorithms to train and evaluate their performance. It comprises 1,500 images that cover 60 distinct waste classes, including items like aluminum_foil, batterie, and aluminum_blister_pack.

TACO contains high-resolution images, taken mostly by mobile phones. These are managed and stored by Flickr, whereas the authors’ server manages the annotations and runs periodically a crawler to collect more potential images of litter. Additionally, the authors also selected some images from source. All images are under free copyright licenses and are annotated and segmented by users using online tool.

image

Distribution of image resolutions.

Specifically, images are labeled with the scene tags to describe their background – these are not mutually exclusive – and litter instances are segmented and labeled using a hierarchical taxonomy with 60 categories of litter which belong to 28 super (top) categories, including a special category: Unlabeled litter for objects that are either ambiguous or not covered by the other categories.

image

Proportion of images by background tag.

The authors targeted 9 super categories based on the number of instances and merged the rest under the class name Other Litter. Authors call this TACO-10 and the Figure below shows the size variability of annotations per category for this new taxonomy. Authors can see that most of the cigarettes, the third largest class, have an area of less than 64×64 pixels.

image

Histogram of bounding box sizes per category for TACO-10.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

TACO: Trash Annotations in Context is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the waste recycling industry.

The dataset consists of 1500 images with 9823 labeled objects belonging to 60 different classes including plastic_film, unlabeled_litter, cigarette, and other: clear_plastic_bottle, plastic_bottle_cap, other_plastic_wrapper, other_plastic, drink_can, plastic_straw, disposable_plastic_cup, other_carton, styrofoam_piece, glass_bottle, pop_tab, plastic_lid, normal_paper, paper_cup, metal_bottle_cap, single-use_carrier_bag, other_plastic_bottle, aluminium_foil, drink_carton, corrugated_carton, disposable_food_container, tissues, crisp_packet, plastic_utensils, rope&strings, and 32 more.

Images in the TACO dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. Also the dataset includes batch and scene image-level tags, supercategory object tag. The dataset was released in 2019.

Here is a visualized example for randomly selected sample classes:

Explore #

TACO dataset has 1500 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
OpenSample annotation mask from TACOSample image from TACO
👀
Have a look at 1500 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 60 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 60
Class
Images
Objects
Count on image
average
Area on image
average
plastic_film
any
310
957
3.09
6.18%
unlabeled_litter
any
269
1068
3.97
1%
cigarette
any
227
1336
5.89
0.24%
clear_plastic_bottle
any
225
576
2.56
4.42%
plastic_bottle_cap
any
185
419
2.26
0.54%
other_plastic_wrapper
any
184
583
3.17
4.4%
other_plastic
any
171
563
3.29
1.62%
drink_can
any
151
460
3.05
3.58%
plastic_straw
any
110
336
3.05
2.92%
disposable_plastic_cup
any
91
210
2.31
4.17%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 59
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
cigarette
any
1336
0.06%
13.14%
0%
5px
0.2%
1039px
33.3%
57px
1.75%
11px
0.34%
1642px
39.47%
unlabeled_litter
any
1068
0.37%
54.21%
0%
7px
0.29%
2584px
82.82%
129px
4.06%
13px
0.46%
2723px
82.08%
plastic_film
any
957
3.11%
76.89%
0%
20px
0.48%
3189px
99.97%
420px
12.91%
23px
0.89%
3559px
92.44%
other_plastic_wrapper
any
583
2.2%
58.36%
0%
18px
0.43%
3483px
83.73%
352px
10.37%
33px
1.01%
3125px
83.94%
clear_plastic_bottle
any
576
2.75%
71.12%
0.01%
29px
1.1%
3996px
96.06%
450px
14.17%
34px
1.39%
3689px
88.68%
other_plastic
any
563
0.73%
36.08%
0%
12px
0.49%
2008px
64.36%
194px
6.17%
13px
0.43%
2653px
63.77%
drink_can
any
460
1.97%
34.16%
0%
22px
0.68%
2526px
60.72%
362px
11.57%
24px
0.76%
3083px
76.59%
plastic_bottle_cap
any
419
0.4%
10.63%
0%
15px
0.38%
1219px
30.23%
150px
4.5%
14px
0.49%
1129px
37.78%
plastic_straw
any
336
1.08%
44.6%
0%
7px
0.21%
2981px
71.66%
339px
10.03%
12px
0.65%
2146px
62.24%
broken_glass
any
280
0.19%
12.48%
0%
14px
0.57%
1213px
37.16%
91px
2.76%
14px
0.46%
1097px
36.28%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 9823 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 9823
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
plastic_bottle_cap
any
batch_4_000000.JPG
3264 x 2448
66px
2.02%
71px
2.9%
0.04%
2
plastic_bottle_cap
any
batch_4_000000.JPG
3264 x 2448
66px
2.02%
71px
2.9%
0.06%
3
rope&strings
any
batch_10_000069.jpg
1824 x 4000
126px
6.91%
147px
3.67%
0.07%
4
rope&strings
any
batch_10_000069.jpg
1824 x 4000
126px
6.91%
147px
3.67%
0.25%
5
other_plastic_cup
any
batch_4_000026.JPG
3264 x 2448
241px
7.38%
470px
19.2%
0.61%
6
other_plastic_cup
any
batch_4_000026.JPG
3264 x 2448
241px
7.38%
470px
19.2%
1.42%
7
plastic_film
any
batch_4_000088.JPG
3264 x 2448
160px
4.9%
334px
13.64%
0.55%
8
plastic_film
any
batch_4_000088.JPG
3264 x 2448
160px
4.9%
334px
13.64%
0.67%
9
plastic_film
any
batch_4_000088.JPG
3264 x 2448
180px
5.51%
216px
8.82%
0.28%
10
plastic_film
any
batch_4_000088.JPG
3264 x 2448
180px
5.51%
216px
8.82%
0.49%

License #

TACO: Trash Annotations in Context is under CC BY 4.0 license.

Source

Citation #

If you make use of the TACO data, please cite the following reference:

@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}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-taco-dataset,
  title = { Visualization Tools for TACO Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/taco } },
  url = { https://datasetninja.com/taco },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset TACO can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='TACO', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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