--- license: cc-by-4.0 task_categories: - object-detection language: - en - zh tags: - magnetic-tile - surface-defect - defect-detection - industrial-inspection - object-detection - pascal-voc - yolo - keenforge size_categories: - 1K ✅ **Upstream license: Creative Commons Attribution 4.0 International (CC BY 4.0)**. This release maintains the same open license. ### Corrections vs. the official release | # | Correction | Detail | |---|---|---| | 1 | **Added Bounding Boxes (YOLO + Pascal VOC)** | Upstream released **only grayscale saliency masks (0–255)** and zero bounding boxes. Adaptive Otsu thresholding and morphological closing were applied to extract tight, high-precision bounding boxes (437 BBoxes) across all 5 defect categories. | | 2 | **Removed 22 Burst-Shot Duplicates** | Industrial cameras captured identical tiles in rapid burst intervals (e.g. `exp2_num_86854` vs `exp2_num_85846`, MAE = 1.64). **22 near-identical temporal duplicates were isolated into `_removed_duplicates/`**, preventing evaluation data leakage between splits. | | 3 | **Removed 4 Zero-Defect Dirty Images** | In category `MT_Uneven`, 4 images were labeled as defective but had 100% black masks (0 foreground defect pixels: `exp3_num_45042`, `exp4_num_124690`, `exp4_num_352527`, `exp4_num_45057`). Removed to prevent harmful negative-supervision. | | 4 | **Added Reproducible Stratified Split** | Partitioned images into **Train (70%) / Val (10%) / Test (20%)** using seeded stratified sampling to guarantee rare classes (such as `Fray`) are well-represented across all splits. | | 5 | **Standard Packaging** | Added `data.yaml`, `classes.txt`, and synchronized dual-format annotations (`Annotations/` + `labels/`). | | 6 | **Defect-free images ship without a label file** | The 941 `MT_Free` negatives keep their image and their (object-free) VOC XML, but have **no** YOLO `.txt`. In YOLO a missing label file is behaviorally identical to an empty one — both mean "no objects" — and empty files additionally break dataset indexing on Hugging Face. | ### Dataset at a glance | Property | Value | |---|---| | Images (total) | **1,318** (941 defect-free normal negatives, 377 defective) | | Classes | **5** — `Blowhole`, `Break`, `Crack`, `Fray`, `Uneven` | | Bounding boxes | **437** | | Formats | Pascal VOC XML · YOLO TXT | | Split | train **920** (70%) / val **129** (10%) / test **269** (20%) | **Boxes per class:** `Break` 121 · `Blowhole` 113 · `Uneven` 105 · `Crack` 61 · `Fray` 37 ### Structure ``` MagneticTile-corrected/ ├── images/ │ ├── train/ # 920 images │ ├── val/ # 129 images │ └── test/ # 269 images ├── labels/ │ ├── train/ # 262 YOLO TXT files (defective images only) │ ├── val/ # 35 YOLO TXT files │ └── test/ # 80 YOLO TXT files ├── Annotations/ # Pascal VOC XML annotations (1,318 files) ├── JPEGImages/ # Full image collection (1,318 images) ├── classes.txt # 5 defect category names ├── data.yaml # Ultralytics YOLO configuration ├── _KeenForge_MagneticTile_compare.png # Visual inspection proof └── README.md ``` > **Note on label files.** Only the **377 defective** images have a YOLO `.txt` (377 files total). > The **941 defect-free `MT_Free` negatives have no label file** — a missing file and an empty file > are equivalent in YOLO (both = "no objects"), and empty files break dataset indexing. > `Annotations/` still contains a VOC XML for every image (negatives have zero `` entries). ### Quick Start (Ultralytics YOLO) ```bash yolo detect train data=data.yaml model=yolov8n.pt epochs=100 imgsz=640 ``` ### Citation **1. The original dataset — please always cite this.** ```bibtex @article{huang2018saliency, title = {Saliency of magnetic tile surface defects}, author = {Huang, Yibin and Qiu, Congying and Yuan, Kui}, journal = {arXiv preprint arXiv:1805.01168}, year = {2018} } ``` **2. This corrected release — please cite it as well.** ```bibtex @misc{magnetictile_corrected, author = {KeenForgeAI}, title = {MagneticTile-corrected: a cleaned and bounding-box standardized release of the Magnetic Tile Surface Defect dataset}, year = {2026}, version = {1.0}, publisher = {KeenForgeAI}, url = {https://huggingface.co/datasets/KeenForgeAI/MagneticTile-corrected}, note = {Curated by Lu Gan and Sam Li. Derived from Huang et al. (2018), arXiv:1805.01168. CC BY 4.0 licensed.} } ``` **3. The annotation and quality inspection tool.** ```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 **CC BY 4.0** — Creative Commons Attribution 4.0 International, matching the upstream repository's terms. Please also credit the original authors. --- ## 中文 ### 这是什么？ **磁瓦表面缺陷数据集（Magnetic Tile Surface Defect Dataset）**（Huang 等，*arXiv:1805.01168*，2018）的**目标检测清洗与标准化重构版**。 原始数据集仅提供连续灰度显著性掩码（Mask PNG），缺少矩形目标检测标注框（BBox）；且工业相机连拍导致大量高相似度冗余帧，容易造成评估集数据泄漏；此外还存在纯黑空标脏样本以及目录结构割裂等问题。 本版本利用 **KeenForge** 质检引擎对原始数据进行去重与清洗，通过自适应分割算法提取紧密 BBox，补充 **YOLO TXT** 与 **Pascal VOC XML** 双格式标注，剔除 22 张时序连拍冗余图与 4 张纯黑空标脏图，并提供开箱即用的分层划分与训练配置文件。 > ✅ **上游许可证：CC BY 4.0**。本版沿用同一开源协议。 ### 相对官方版的修正 | # | 修正项 | 说明 | |---|---|---| | 1 | **补充检测框（YOLO + Pascal VOC）** | 官方仅发布连续灰度掩码（0~255），无任何目标检测框。本版采用自适应分割与连通域提取，在 5 类缺陷中生成 **437 个高精度 Bounding Box 矩形框**。 | | 2 | **剔除 22 张连拍重复图** | 工业相机在微秒级间隔抓拍了几乎相同的工件（如 `exp2_num_86854` 与 `exp2_num_85846` 平均像素差仅 1.64）。本版将 **22 张时序冗余帧隔离至 `_removed_duplicates/`**，彻底根除划分集间的数据泄漏。 | | 3 | **删除 4 张纯黑空标脏图** | 在 `MT_Uneven` 类中发现 4 张标记为缺陷但掩码全黑（0 缺陷像素）的异常图（`exp3_num_45042`、`exp4_num_124690`、`exp4_num_352527`、`exp4_num_45057`），已全部剔除，杜绝负向误学习。 | | 4 | **补充可复现的分层划分** | 采用随机种子按 **70% 训练 / 10% 验证 / 20% 测试** 进行分层抽样，确保稀缺类别（如 `Fray` 磨损）在验证与测试集中稳定存在。 | | 5 | **标准化打包** | 补充 `data.yaml`、`classes.txt` 以及标准组织目录，支持主流框架直接训练。 | | 6 | **无缺陷图不附带标签文件** | 941 张 `MT_Free` 正常底板保留图片与（无目标的）VOC XML，但**不提供** YOLO `.txt`。在 YOLO 中"缺少标签文件"与"空标签文件"完全等价（均表示无目标），而空文件还会导致 Hugging Face 数据集索引失败。 | ### 数据集概览 | 属性 | 值 | |---|---| | 图像总数 | **1,318**（941 张无缺陷正常底板，377 张缺陷样本） | | 缺陷类别 | **5** —— `Blowhole`（气孔）、`Break`（断裂）、`Crack`（裂纹）、`Fray`（磨损）、`Uneven`（表面不平） | | 标注框总数 | **437** | | 标注格式 | Pascal VOC XML · YOLO TXT | | 划分集 | train **920**（70%） / val **129**（10%） / test **269**（20%） | **各类框数：** `Break` 121 · `Blowhole` 113 · `Uneven` 105 · `Crack` 61 · `Fray` 37 ### 目录结构 ``` MagneticTile-corrected/ ├── images/ │ ├── train/ # 920 张图片 │ ├── val/ # 129 张图片 │ └── test/ # 269 张图片 ├── labels/ │ ├── train/ # 262 个 YOLO TXT 标注文件（仅缺陷图） │ ├── val/ # 35 个 YOLO TXT 标注文件 │ └── test/ # 80 个 YOLO TXT 标注文件 ├── Annotations/ # Pascal VOC XML 标注 (1,318 个文件) ├── JPEGImages/ # 完整图片集合 (1,318 张图片) ├── classes.txt # 5 个缺陷类别名称 ├── data.yaml # Ultralytics YOLO 配置文件 ├── _KeenForge_MagneticTile_compare.png # 清洗与框提取效果对比图 └── README.md ``` > **关于标签文件。** 仅 **377 张缺陷图** 有 YOLO `.txt` 标签（合计 377 个文件）。 > **941 张无缺陷 `MT_Free` 正常底板不附带标签文件** —— YOLO 中"缺少文件"与"空文件"等价（均表示无目标）， > 且空文件会导致数据集索引失败。`Annotations/` 仍为每张图保留 VOC XML（正常底板的 `` 为空）。 ### 快速开始 (Ultralytics YOLO) ```bash yolo detect train data=data.yaml model=yolov8n.pt epochs=100 imgsz=640 ``` ### 引用 **1. 原始数据集（请务必引用）** —— 见上方英文部分 `huang2018saliency`。 **2. 本修正版（请一并引用）** —— 见上方英文部分 `magnetictile_corrected`。 **3. 标注与质检工具（可选）** —— 见上方英文部分 `keenforge`。 ### 许可证 **CC BY 4.0** —— 与上游 Magnetic Tile 数据集开源协议一致。请同时注明原作者与本整理版本。