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Water Meters Dataset

124412531
Tagenergy-and-utilities
Tasksemantic segmentation
Release YearMade in 2020
LicenseCC BY-NC-ND 4.0
Download306 MB

Introduction #

Roman Kucev

The Water Meters Dataset comprises a wide array of images depicting water meters, complete with associated segmentation masks and OCR labels for meter readings. The data was collected using Yandex.Toloka. Please note that this description is just a sample of the data. For comprehensive information, refer to the dataset’s homepage.

Dataset LinkHomepageDataset LinkBlog Post

Summary #

Water Meters Dataset is a dataset for semantic segmentation and object detection tasks. It is used in the utilities industry.

The dataset consists of 1244 images with 1244 labeled objects belonging to 1 single class (water meter data).

Images in the Water Meters dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2020 by the TrainingData.pro, UAE.

Dataset Poster

Explore #

Water Meters dataset has 1244 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 Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
OpenSample annotation mask from Water MetersSample image from Water Meters
πŸ‘€
Have a look at 1244 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 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 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
water meter dataβž”
mask
1244
1244
1
2.31%

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 1-1 of 1
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
water meter data
mask
1244
2.31%
15.09%
0.28%
31px
2.33%
679px
67.9%
141px
11.22%
47px
3.53%
947px
79.2%

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 1244 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 1244
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”
water meter data
mask
id_656_value_143_044.jpg
1000 x 1333
80px
8%
336px
25.21%
1.86%
2βž”
water meter data
mask
id_46_value_647_254.jpg
1333 x 1000
106px
7.95%
362px
36.2%
1.67%
3βž”
water meter data
mask
id_28_value_81_998.jpg
1333 x 1000
72px
5.4%
410px
41%
1.83%
4βž”
water meter data
mask
id_1042_value_30_011.jpg
1333 x 1000
52px
3.9%
267px
26.7%
0.82%
5βž”
water meter data
mask
id_237_value_15_381.jpg
1333 x 1000
115px
8.63%
479px
47.9%
3.87%
6βž”
water meter data
mask
id_230_value_80_351.jpg
1333 x 1000
113px
8.48%
375px
37.5%
2.11%
7βž”
water meter data
mask
id_763_value_169_244.jpg
1333 x 1000
135px
10.13%
509px
50.9%
4.68%
8βž”
water meter data
mask
id_35_value_469_334.jpg
1333 x 1000
94px
7.05%
392px
39.2%
2.42%
9βž”
water meter data
mask
id_731_value_231_041.jpg
1333 x 1000
93px
6.98%
305px
30.5%
1.57%
10βž”
water meter data
mask
id_97_value_368_218.jpg
1778 x 1000
97px
5.46%
374px
37.4%
1.39%

License #

Water Meters Dataset is under CC BY-NC-ND 4.0 license.

Source

Citation #

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

@dataset{Water Meters,
	author={Kucev Roman},
	title={Water Meters Dataset},
	year={2020},
	url={https://www.kaggle.com/datasets/tapakah68/yandextoloka-water-meters-dataset}
}

Source

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

@misc{ visualization-tools-for-water-meters-dataset,
  title = { Visualization Tools for Water Meters Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/water-meters } },
  url = { https://datasetninja.com/water-meters },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-03 },
}

Download #

Dataset Water Meters 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='Water Meters', 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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