Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Size:
10K - 100K
Tags:
ball-screw
surface-defect
defect-classification
industrial-inspection
quality-control
manufacturing
DOI:
License:
| license: cc-by-sa-4.0 | |
| task_categories: | |
| - image-classification | |
| language: | |
| - en | |
| - zh | |
| tags: | |
| - ball-screw | |
| - surface-defect | |
| - defect-classification | |
| - industrial-inspection | |
| - quality-control | |
| - manufacturing | |
| - image-classification | |
| - keenforge | |
| size_categories: | |
| - 10K<n<100K | |
| # KIT_BallScrew_Defects-corrected | |
| [](https://doi.org/10.57967/hf/11010) | |
| **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](#english) · [中文](#中文) | |
| --- | |
| ## 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](https://github.com/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](#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) | |
| ```python | |
| 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.** | |
| ```bibtex | |
| @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.** | |
| ```bibtex | |
| @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).** | |
| ```bibtex | |
| @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. | |
| --- | |
| ## 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](https://github.com/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](#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) | |
| ```python | |
| 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 | |