Dataset Ninja LogoDataset Ninja:

Supervisely Synthetic Crack Segmentation Dataset

155713448
Tagenergy-and-utilities, featured, tutorial
Tasksemantic segmentation
Release YearMade in 2023
LicenseCC BY-NC 4.0
Download206 MB

Introduction #

Supervisely Synthetic Crack Segmentation is a dataset for a semantic segmentation of cracks in industrial inspection. Obtaining real-world annotated data for crack segmentation can be challenging. The detailed, pixel-perfect nature of segmentation requires extensive labor and often expert knowledge, making the process time-consuming and costly. Synthetic data offers a promising solution to these challenges. It provides a controlled, cost-effective, and automated alternative to real-world data collection and manual annotation.

Learn more in the supervisely blog post.

ExpandExpand
Dataset LinkHomepage

Summary #

Supervisely Synthetic Crack Segmentation is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the industrial domain.

The dataset consists of 1557 images with 1550 labeled objects belonging to 1 single class (cracks).

Images in the Supervisely Synthetic Crack Segmentation dataset have pixel-level semantic segmentation annotations. There are 7 (0% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: synthetic cracks (1157 images) and synthetic cracks styled (400 images). The dataset was released in 2023 by the Supervisely.

Here is the visualized example grid with animated annotations:

Explore #

Supervisely Synthetic Crack Segmentation dataset has 1557 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 Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
OpenSample annotation mask from Supervisely Synthetic Crack SegmentationSample image from Supervisely Synthetic Crack Segmentation
πŸ‘€
Have a look at 1557 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
cracksβž”
mask
1550
1550
1
2.21%

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
cracks
mask
1550
2.21%
10.71%
0.01%
16px
3.12%
512px
100%
351px
68.55%
19px
3.71%
512px
100%

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 1550 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 1550
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”
cracks
mask
00_00009_5775942379_45578dea0c_o.jpg
512 x 512
415px
81.05%
289px
56.45%
1.32%
2βž”
cracks
mask
02_00234_4858932192_0b4761d7ef_o.jpg
512 x 512
448px
87.5%
469px
91.6%
3.1%
3βž”
cracks
mask
02_00393_6991918872_bb03743ab9_o.jpg
512 x 512
431px
84.18%
443px
86.52%
3.69%
4βž”
cracks
mask
05_00149_pexels_978462.jpeg
512 x 512
72px
14.06%
208px
40.62%
0.42%
5βž”
cracks
mask
00_00047_5877307859_106c6ae9dc_o.jpg
512 x 512
306px
59.77%
299px
58.4%
1.1%
6βž”
cracks
mask
05_00038_3192726380_9e225c1496_o.jpg
512 x 512
400px
78.12%
383px
74.8%
3.46%
7βž”
cracks
mask
00_00176_pexels_4604569.jpeg
512 x 512
378px
73.83%
343px
66.99%
2.17%
8βž”
cracks
mask
05_00008_pexels_7078272.jpeg
512 x 512
475px
92.77%
267px
52.15%
2.65%
9βž”
cracks
mask
04_00049_pexels_7599120.jpeg
512 x 512
364px
71.09%
368px
71.88%
1.6%
10βž”
cracks
mask
04_00047_5877307859_106c6ae9dc_o.jpg
512 x 512
200px
39.06%
450px
87.89%
0.74%

License #

Supervisely Synthetic Crack Segmentation is under CC BY-NC 4.0 license.

Source

Citation #

If you make use of the Supervisely Synthetic Crack Segmentation data, please cite the following reference:

@dataset{Supervisely Synthetic Crack Segmentation,
	author={Supervisely},
	title={Supervisely Synthetic Crack Segmentation},
	year={2023},
	url={https://supervisely.com/blog/introducing-supervisely-synthetic-crack-segmentation-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-synthetic-cracks-dataset-dataset,
  title = { Visualization Tools for Supervisely Synthetic Crack Segmentation Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/synthetic-cracks-dataset } },
  url = { https://datasetninja.com/synthetic-cracks-dataset },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-03 },
}

Download #

Dataset Supervisely Synthetic Crack Segmentation 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='Supervisely Synthetic Crack Segmentation', 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 #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.