Note

This is a Hugging Face dataset. For large datasets, ensure huggingface_hub>=1.1.3 to avoid rate limits. Learn more in the Hugging Face integration docs.

Dataset Card for Kolektor Surface-Defect Dataset#

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KolektorSDD (Kolektor Surface-Defect Dataset) is a grayscale industrial surface-inspection dataset of electrical commutators.

This FiftyOne dataset uses the box-annotation release intended for the ICPR 2021 and COMIND 2021 papers (download): one sample per surface image, with defect regions annotated as axis-aligned bounding boxes stored as filled rectangles in the label masks.

This is a FiftyOne dataset with 399 samples.

Installation#

If you haven’t already, install FiftyOne:

pip install -U fiftyone

Usage#

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/Kolektor_Surface_Defect")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details#

Curated by: Domen Tabernik, Samo Šela, Jure Skvarč, Danijel Skočaj (University of Ljubljana / ViCoS Lab); images provided and annotated by Kolektor Group d.o.o.
Paper (dataset): Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
Box annotations used in: End-to-end training of a two-stage neural network for defect detection (ICPR 2020) and Mixed supervision for surface-defect detection (Computers in Industry, 2021)
Project page: https://www.vicos.si/resources/kolektorsdd/
Download (this release): https://go.vicos.si/kolektorsddboxes
License: CC BY-NC-SA 4.0 (non-commercial; contact Danijel Skočaj for commercial use)

What the data contains#

Images were captured in a controlled industrial environment. Each sample is one non-overlapping view of a commutator surface. Defects are microscopic fractures or cracks in the plastic embedding.

Property	Value
Total images	399
Physical items (boards)	50 (`kos01`–`kos50`)
Surfaces per item	8 (`Part0`–`Part7`)
Defective images	52
Non-defective images	347
Image type	Grayscale JPG
Original size	500 px wide × 1240–1270 px tall
Recommended eval size	512 × 1408 px (per dataset authors)

Defect visibility: for 48 items the defect appears in exactly one image; for 2 items it appears in two images.

A separate fine pixel-annotation release exists for the JIM2019 paper (download). That version is not what this card describes.

Raw download layout#

kolektorsdd/
  kos01/
    Part0.jpg
    Part0_label.bmp
    Part1.jpg
    Part1_label.bmp
    ...
  kos02/
    ...

Part*.jpg — surface image
Part*_label.bmp — defect annotation mask (non-zero = defect region)

In this box-annotation release, each defective mask is a filled axis-aligned bounding box around the defect, not a precise pixel-wise segmentation of the crack shape.

Train/test splits#

The authors evaluate with 3-fold cross-validation, keeping all 8 images of the same physical item in the same fold. Official split files: KolektorSDD-training-splits.zip.

This FiftyOne dataset does not assign fold/split labels. Add them externally if needed.

FiftyOne Dataset Structure#

Property	Value
Hub dataset	`harpreetsahota/Kolektor_Surface_Defect`
Local dataset name	`kolektorsdd`
Media type	`image`
Samples	399

Sample fields#

Field	Type	Description
`filepath`	`StringField`	Path to source `Part*.jpg`
`board_id`	`StringField`	Board directory name, e.g. `"kos01"`
`has_defect`	`BooleanField`	`True` if the mask contains any foreground pixel
`ground_truth`	`EmbeddedDocumentField(Segmentation)`	Binarized mask (`0` = background, `1` = defect)

The local parser (parse_to_fo.py) reads each BMP label and stores a {0, 1} mask on the sample. For defective images in this release, the foreground region is a filled bounding box rather than a tight defect outline.

Citation#

BibTeX (dataset):

@article{Tabernik2019JIM,
  author  = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and Sko{\v{c}}aj, Danijel},
  journal = {Journal of Intelligent Manufacturing},
  title   = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}},
  year    = {2019},
  month   = {May},
  day     = {15},
  issn    = {1572-8145},
  doi     = {10.1007/s10845-019-01476-x}
}

BibTeX (box annotations / mixed supervision):

@article{Bozic2021COMIND,
  author  = {Bo{\v{z}}i{\v{c}}, Jakob and Tabernik, Domen and Sko{\v{c}}aj, Danijel},
  journal = {Computers in Industry},
  title   = {{Mixed supervision for surface-defect detection: from weakly to fully supervised learning}},
  year    = {2021}
}

APA:

Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2019). Segmentation-Based Deep-Learning Approach for Surface-Defect Detection. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01476-x