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Drinking Waste Dataset

48164563
Tagenvironmental
Taskobject detection
Release YearMade in 2023
LicenseCC0 1.0
Download756 MB

Introduction #

Arkadiy Serezhkin

This Drinking Waste Dataset was manually labelled and collected as a part of author final year Individual Project at University College London. Pictures were taken with 12 MP phone camera. He created a real-time waste detection and identification system powered by YOLO framework. The dataset used images from Trashnet classification dataset.

Dataset LinkHomepage

Summary #

Drinking Waste Dataset is a dataset for an object detection task. Possible applications of the dataset could be in the environmental industry.

The dataset consists of 4816 images with 5058 labeled objects belonging to 4 different classes including plastic bottle, glass bottle, aluminium can, and other: plastic milk bottle.

Images in the Drinking Waste dataset have bounding box annotations. There are 5 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2023.

Dataset Poster

Explore #

Drinking Waste dataset has 4816 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 Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
OpenSample annotation mask from Drinking WasteSample image from Drinking Waste
πŸ‘€
Have a look at 4816 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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 1-4 of 4
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
plastic bottleβž”
rectangle
1496
1496
1
16.95%
glass bottleβž”
rectangle
1229
1402
1.14
21.92%
aluminium canβž”
rectangle
1083
1128
1.04
9.71%
plastic milk bottleβž”
rectangle
1031
1032
1
8.09%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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 1-4 of 4
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
plastic bottle
rectangle
1496
16.95%
99.28%
1.17%
40px
5.86%
547px
100%
218px
38.09%
49px
9.57%
505px
100%
glass bottle
rectangle
1402
19.23%
99.8%
0.56%
31px
4.54%
2447px
100%
210px
39.57%
29px
5.66%
2100px
100%
aluminium can
rectangle
1128
9.32%
96.51%
0.7%
44px
6.44%
384px
100%
146px
24.42%
36px
7.03%
512px
100%
plastic milk bottle
rectangle
1032
8.15%
97.41%
0.76%
44px
6.44%
386px
100%
151px
23.83%
39px
7.62%
511px
99.8%

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 5058 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 5058
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
plastic milk bottle
rectangle
HDPEM118.jpg
683 x 512
211px
30.89%
127px
24.8%
7.66%
2βž”
plastic bottle
rectangle
PET1,716.jpg
384 x 512
281px
73.18%
111px
21.68%
15.86%
3βž”
plastic milk bottle
rectangle
HDPEM175.jpg
683 x 512
180px
26.35%
125px
24.41%
6.43%
4βž”
plastic milk bottle
rectangle
HDPEM641.jpg
683 x 512
109px
15.96%
79px
15.43%
2.46%
5βž”
plastic bottle
rectangle
PET741.jpg
683 x 512
401px
58.71%
199px
38.87%
22.82%
6βž”
plastic bottle
rectangle
PET1,529.jpg
512 x 384
340px
66.41%
183px
47.66%
31.65%
7βž”
glass bottle
rectangle
Glass1,113.jpg
683 x 512
52px
7.61%
117px
22.85%
1.74%
8βž”
glass bottle
rectangle
Glass1,113.jpg
683 x 512
122px
17.86%
85px
16.6%
2.97%
9βž”
glass bottle
rectangle
Glass1,113.jpg
683 x 512
94px
13.76%
105px
20.51%
2.82%
10βž”
plastic bottle
rectangle
PET1,283.jpg
683 x 512
188px
27.53%
138px
26.95%
7.42%

License #

Drinking Waste Dataset is under CC0 1.0 license.

Source

Citation #

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

@dataset{Drinking Waste,
  author={Arkadiy Serezhkin},
  title={Drinking Waste Dataset},
  year={2023},
  url={https://www.kaggle.com/datasets/arkadiyhacks/drinking-waste-classification}
}

Source

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

@misc{ visualization-tools-for-drinking-waste-dataset,
  title = { Visualization Tools for Drinking Waste Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/drinking-waste } },
  url = { https://datasetninja.com/drinking-waste },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { apr },
  note = { visited on 2024-04-14 },
}

Download #

Dataset Drinking Waste 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='Drinking Waste', 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.

. . .

Disclaimer #

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