Voxel51/Kolektor_Surface_Defect

Dataset Card for Kolektor Surface-Defect Dataset

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.

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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.

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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