Introduction #
The Fabric Stain Dataset has been developed to facilitate the classification of fabric stain defects in textile quality control. This dataset is an integral component of the fabric defect detection project undertaken by the Intellisense Lab at the University of Moratuwa, Sri Lanka.
| Information | Content |
|---|---|
| Number of categories | 2 (stain, defect-free) |
| Number of stain images | 398 |
| Number of defect-free images | 68 |
| Image resolution | 1920x1080 or 1080x1920 |
| Stain types | ink stain, dirt stain, oil stain |
| Fabric types | polyester, cotton |
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HomepageSummary #
Fabric Stain Dataset is a dataset for object detection and classification tasks. It is used in the textile industry.
The dataset consists of 466 images with 870 labeled objects belonging to 1 single class (stain).
Images in the Fabric Stain dataset have bounding box annotations. There are 68 (15% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 2 classification categories: stain (398 images) and defect free (68 images). The dataset was released in 2020 by the Intellisense Lab of University of Moratuwa, Sri Lanka.
Explore #
Fabric Stain dataset has 466 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.
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.
Class ã…¤ | Images ã…¤ | Objects ã…¤ | Count on image average | Area on image average |
|---|---|---|---|---|
stainâž” rectangle | 398 | 870 | 2.19 | 14.52% |
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.
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
stain rectangle | 870 | 6.66% | 63.92% | 0.03% | 15px | 1.01% | 1984px | 100% | 378px | 22.6% | 13px | 0.66% | 1724px | 89.52% |
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.
Objects #
Table contains all 870 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.
Object ID ã…¤ | Class ã…¤ | Image name click row to open | Image size height x width | Height ã…¤ | Height ã…¤ | Width ã…¤ | Width ã…¤ | Area ã…¤ |
|---|---|---|---|---|---|---|---|---|
1âž” | stain rectangle | stain_188.jpg | 1984 x 1488 | 733px | 36.95% | 503px | 33.8% | 12.49% |
2âž” | stain rectangle | stain_188.jpg | 1984 x 1488 | 417px | 21.02% | 685px | 46.03% | 9.68% |
3âž” | stain rectangle | stain_188.jpg | 1984 x 1488 | 516px | 26.01% | 213px | 14.31% | 3.72% |
4âž” | stain rectangle | stain_336.jpg | 1488 x 1984 | 499px | 33.53% | 879px | 44.3% | 14.86% |
5âž” | stain rectangle | stain_184.jpg | 1984 x 1488 | 146px | 7.36% | 98px | 6.59% | 0.48% |
6âž” | stain rectangle | stain_184.jpg | 1984 x 1488 | 73px | 3.68% | 65px | 4.37% | 0.16% |
7âž” | stain rectangle | stain_184.jpg | 1984 x 1488 | 105px | 5.29% | 91px | 6.12% | 0.32% |
8âž” | stain rectangle | stain_184.jpg | 1984 x 1488 | 100px | 5.04% | 287px | 19.29% | 0.97% |
9âž” | stain rectangle | stain_184.jpg | 1984 x 1488 | 138px | 6.96% | 54px | 3.63% | 0.25% |
10âž” | stain rectangle | stain_14.jpg | 1488 x 1984 | 351px | 23.59% | 364px | 18.35% | 4.33% |
License #
Citation #
If you make use of the Fabric Stain Dataset data, please cite the following reference:
@dataset{Fabric Stain Dataset,
author={Primesh Pathirana},
title={Fabric Stain Dataset},
year={2020},
url={https://www.kaggle.com/datasets/priemshpathirana/fabric-stain-dataset}
}
If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:
@misc{ visualization-tools-for-fabric-stain-dataset-dataset,
title = { Visualization Tools for Fabric Stain Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/fabric-stain-dataset } },
url = { https://datasetninja.com/fabric-stain-dataset },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { oct },
note = { visited on 2024-10-31 },
}Download #
Dataset Fabric Stain 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='Fabric Stain', 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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