KIT_BallScrew_Defects-corrected

Ball-screw surface defect classification — cleaned, re-indexed, split and standardized version of the KIT-BS-recls dataset. 滚珠丝杠表面缺陷分类** —— KIT-BS-recls 数据集的清理、重编号、划分与规整版。**

English · 中文

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English

What is this?

A cleaned, re-indexed, split and standardized image-classification version of the Tri-classified Dataset for Ball Screw Surface Defects (KIT-BS-recls) — three surface conditions: pitting, contaminated and normal. This is a classification dataset (one label per image, no bounding boxes).

Reference: Xue, F., Zhang, W., Xie, S., Puchta, A. & Fleischer, J. "Domain-unique group- and its subgroups-aware fault diagnostics for machine components: An open-set domain adaptation approach." Neurocomputing, 2025 — https://doi.org/10.1016/j.neucom.2025.131291. Upstream: fenghsueh/Triclass-BSDefects (also on Kaggle).

The original release ships as three class folders plus a single labels.txt that maps each path to a label, where the labels are the non-contiguous ids 3 / 6 / 8, with no train/val/test split and filenames containing parentheses (P(123).png).

This release converts the archive into a folder-based classification layout, remaps the labels to 0 / 1 / 2, adds a reproducible stratified split and normalises the filenames.

✅ Upstream license: CC BY-SA 4.0 (Attribution-ShareAlike). This is a real open licence, but it is ShareAlike — any derivative must be released under the same licence. See License & attribution.

Corrections vs. the official release

#	Correction	Detail
1	Classification layout	Converted the archive into images/<split>/<class>/ (Ultralytics-style folder classification). The redundant labels.txt is replaced by labels.csv and the folder structure.
2	Re-indexed labels ⭐	The upstream ids are the non-contiguous 3 / 6 / 8 (pitting / contaminated / normal). They are remapped to the contiguous 0 / 1 / 2, so a model's output layer has no wasted units.
3	Reproducible split	Upstream ships no split. This release provides a seeded, class-stratified 70 / 15 / 15 split (seed 42): 15,288 / 3,276 / 3,271.
4	Near-duplicates kept together	Near-identical images (identical 8×8-dHash and 32×32 pixel-MSE ≤ 1) are grouped and assigned entirely to one split (50 such groups).
5	Filename hygiene	Filenames contained parentheses, e.g. P(123).png → P_123.png; P(1000)_.png → P_1000_.png.
6	Packaging	Added classes.txt, data.yaml (Ultralytics classification) and labels.csv (filename, split, class, index, original name/dir).

Image pixels are never modified. All 21,835 images are byte-for-byte identical to upstream (150×150 RGB .png; 9 images differ slightly in size — see note).

Scope — structural clean-up only. This is not a re-annotation; each image keeps its original single class label.

Structural checks performed on the released set:

Check	Method	Result
labels.txt ↔ class folder consistency	per line	0 mismatches
Files in samples/ missing from labels.txt	set diff	0
Exact duplicate images	md5	0
Cross-split near-identical leakage	cluster assignment	0 groups straddle a split
Image format / channels	scan	RGB PNG, 150×150 (9 images slightly off-size)

Dataset at a glance

Property	Value
Images	21,835 (150 × 150 RGB .png)
Task	Image classification (single label per image)
Classes	3 — pitting (0), contaminated (1), normal (2)
Split	train 15,288 / val 3,276 / test 3,271 (seeded, class-stratified)

Images per class: pitting 10,760 · contaminated 7,523 · normal 3,552

Per split: train (pitting 7,532 / contaminated 5,267 / normal 2,489) · val (1,614 / 1,130 / 532) · test (1,614 / 1,126 / 531)

Note: 9 of the 21,835 images are not exactly 150×150 (e.g. 146×150, 127×150). Their pixels are left unmodified; resize on load if a fixed input size is required.

Structure

KIT_BallScrew_Defects-corrected/
├── images/
│   ├── train/{pitting,contaminated,normal}/   # 150x150 RGB .png
│   ├── val/{pitting,contaminated,normal}/
│   └── test/{pitting,contaminated,normal}/
├── classes.txt     # pitting / contaminated / normal (one per line, index order)
├── data.yaml       # Ultralytics classification configuration
├── labels.csv      # filename,split,class,class_index,original_name,original_dir
├── LICENSE         # CC BY-SA 4.0 (full text + notice of changes)
└── README.md

Quick start (Ultralytics)

from ultralytics import YOLO

model = YOLO("yolo11n-cls.pt")
model.train(data=".", epochs=50, imgsz=150)

Citation

1. The original dataset / paper — please always cite this.

@article{xue2025domaingroup,
  title   = {Domain-unique group- and its subgroups-aware fault diagnostics for machine
             components: An open-set domain adaptation approach},
  author  = {Xue, Feng and Zhang, Weimin and Xie, Shulian and Puchta, Alexander and
             Fleischer, J{\"u}rgen},
  journal = {Neurocomputing},
  year    = {2025},
  doi     = {10.1016/j.neucom.2025.131291}
}

2. This corrected release — please cite it as well.

@misc{kit_ballscrew_corrected,
  author    = {KeenForgeAI},
  title     = {KIT_BallScrew_Defects-corrected: a cleaned classification release of the Tri-classified Ball Screw Surface Defects dataset (KIT-BS-recls)},
  year      = {2026},
  version   = {1.0},
  publisher = {KeenForgeAI},
  url       = {https://huggingface.co/datasets/KeenForgeAI/KIT_BallScrew_Defects-corrected},
  note      = {Curated by Lu Gan and Sam Li. Derived from KIT-BS-recls
               (Xue et al., Neurocomputing 2025), licensed CC BY-SA 4.0.}
}

3. The annotation tool (optional).

@software{keenforge,
  author    = {KeenForgeAI},
  title     = {KeenForge: a local-first, offline image annotation and model-training desktop tool},
  year      = {2026},
  publisher = {KeenForgeAI},
  url       = {https://github.com/KeenForgeAI/KeenForge},
  note      = {MIT licensed. Developed by Lu Gan and Sam Li.}
}

License & attribution

CC BY-SA 4.0 (Attribution-ShareAlike 4.0 International). The upstream KIT-BS-recls is licensed under CC BY-SA 4.0. Because ShareAlike is a copyleft condition, this corrected release is distributed under the same CC BY-SA 4.0 licence — you must credit the original authors, indicate if changes were made, and license any derivative under CC BY-SA 4.0. The full licence text and a notice of the changes are in LICENSE.

Original work: Xue, Zhang, Xie, Puchta & Fleischer — https://github.com/fenghsueh/Triclass-BSDefects.

Provenance

• Source: Tri-classified Dataset for Ball Screw Surface Defects (KIT-BS-recls), GitHub fenghsueh/Triclass-BSDefects.
• Upstream: 21,835 images / 3 classes (ids 3/6/8); no split; filenames with parentheses; 0 duplicate images.
• Verified: 21,835 images (byte-identical); labels remapped 3/6/8→0/1/2; split 15288/3276/3271; 0 label/folder mismatches, 0 cross-split near-identical leakage.

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中文

这是什么？

滚珠丝杠表面缺陷三分类数据集（KIT-BS-recls）的清理、重编号、划分与标准化图像分类版—— 三种表面状态：点蚀（pitting）、污染（contaminated）、正常（normal）。这是 分类数据集（每图一个标签，无边界框）。

论文：Xue, F., Zhang, W., Xie, S., Puchta, A. & Fleischer, J.《Domain-unique group- and its subgroups-aware fault diagnostics for machine components: An open-set domain adaptation approach》，Neurocomputing，2025 —— https://doi.org/10.1016/j.neucom.2025.131291。 上游：fenghsueh/Triclass-BSDefects（Kaggle 亦有）。

原始版本以三个类别文件夹 + 单个 labels.txt（每行"路径 + 标签"）发布，标签为不连续的 3 / 6 / 8，无 train/val/test 划分，且文件名含括号（P(123).png）。

本版本将其转换为基于文件夹的分类布局、把标签重映射为 0 / 1 / 2、补充可复现的分层划分， 并净化文件名。

✅ 上游许可证：CC BY-SA 4.0（署名—相同方式共享）。这是真正的开源许可，但为 ShareAlike （传染性）——任何衍生作品必须以同一许可发布。详见 许可证与署名。

相对官方版的修正

#	修正	说明
1	分类布局	转换为 images/<split>/<class>/（Ultralytics 风格的文件夹分类）。冗余的 labels.txt 由 labels.csv 与目录结构取代。
2	标签重编号 ⭐	上游标签为不连续的 3 / 6 / 8（pitting / contaminated / normal），已重映射为连续的 0 / 1 / 2，避免模型输出层出现空洞。
3	可复现划分	上游完全没有划分。本版提供固定种子、按类别分层的 70 / 15 / 15 划分（随机种子 42）：15,288 / 3,276 / 3,271。
4	近重复固定同侧	近重复图（8×8 dHash 相同且 32×32 像素 MSE ≤ 1）归组并整体分配到一个划分（共 50 组）。
5	文件名净化	文件名含括号，如 P(123).png → P_123.png；P(1000)_.png → P_1000_.png。
6	规范化打包	新增 classes.txt、data.yaml（Ultralytics 分类配置）与 labels.csv（文件名、划分、类别、索引、原始名/目录）。

图像像素从不修改——全部 21,835 张图与上游文件逐字节一致（150×150 RGB .png；9 张尺寸略有不同，见说明）。

本版范围——仅结构化清理，不是重新标注；每张图保留其原始单标签。

已执行的结构化检查：labels.txt↔类别目录一致（0 错配）/ samples/ 无遗漏文件 / 0 完全重复 / 0 近重复簇跨越划分 / 全部 RGB PNG。

数据集概览

属性	值
图片	21,835（150 × 150 RGB .png）
任务	图像分类（每图单一标签）
类别	3 —— pitting (0)、contaminated (1)、normal (2)
划分	train 15,288 / val 3,276 / test 3,271（固定种子、类别分层）

各类图像数： pitting 10,760 · contaminated 7,523 · normal 3,552

各划分： train（pitting 7,532 / contaminated 5,267 / normal 2,489）· val（1,614 / 1,130 / 532）· test（1,614 / 1,126 / 531）

说明： 21,835 张中有 9 张并非精确 150×150（如 146×150、127×150）。其像素保持原样； 若需固定输入尺寸，请在加载时缩放。

目录结构

KIT_BallScrew_Defects-corrected/
├── images/
│   ├── train/{pitting,contaminated,normal}/   # 150x150 RGB .png
│   ├── val/{pitting,contaminated,normal}/
│   └── test/{pitting,contaminated,normal}/
├── classes.txt     # pitting / contaminated / normal（每行一个，按索引顺序）
├── data.yaml       # Ultralytics 分类配置
├── labels.csv      # filename,split,class,class_index,original_name,original_dir
├── LICENSE         # CC BY-SA 4.0（全文 + 修改说明）
└── README.md

快速开始 (Ultralytics)

from ultralytics import YOLO

model = YOLO("yolo11n-cls.pt")
model.train(data=".", epochs=50, imgsz=150)

引用

请同时引用原始论文与本修正版：1. 原始论文见上方英文 xue2025domaingroup； 2. 本修正版见上方英文 kit_ballscrew_corrected；3. 标注工具（可选）见 keenforge。

许可证与署名

CC BY-SA 4.0（署名—相同方式共享 4.0 国际）。 上游 KIT-BS-recls 采用 CC BY-SA 4.0。 由于 ShareAlike 是传染性条款，本修正版以同一 CC BY-SA 4.0 许可发布——你必须署名原作者、 注明是否修改，并以 CC BY-SA 4.0 发布任何衍生作品。完整许可文本与修改说明见 LICENSE。

原始作品：Xue, Zhang, Xie, Puchta & Fleischer —— https://github.com/fenghsueh/Triclass-BSDefects。

来源

• 来源：滚珠丝杠表面缺陷三分类数据集（KIT-BS-recls），GitHub fenghsueh/Triclass-BSDefects
• 上游：21,835 图 / 3 类（标签 id 3/6/8）；无划分；文件名含括号；0 完全重复
• 已校验：21,835 图（逐字节一致）；标签 3/6/8→0/1/2；划分 15288/3276/3271；0 标签/目录错配、0 跨划分近重复泄漏