--- license: other license_name: see-license-file-upstream-unstated license_link: LICENSE task_categories: - object-detection language: - en tags: - pku-market-pcb - pcb - pcb-defect - industrial-inspection - object-detection - pascal-voc - yolo - keenforge size_categories: - n<1K --- # PKU-Market-PCB-corrected [![DOI](https://img.shields.io/badge/DOI-10.57967%2Fhf%2F10554-blue)](https://doi.org/10.57967/hf/10554) **PCB defect detection — cleaned and consistently packaged version of the PKU-Market-PCB dataset.** **PCB 缺陷检测 —— PKU-Market-PCB 数据集的清理与规范化打包版。** [English](#english) · [中文](#中文) --- ## English ### What is this? A **cleaned and consistently packaged** version of **PKU-Market-PCB** (Huang, Weibo & Wei, Peng. *"A PCB Dataset for Defects Detection and Classification"*, [arXiv:1901.08204](https://arxiv.org/abs/1901.08204), 2019), released by the **Open Lab on Human Robot Interaction, Peking University**. The images are synthesized from **10 standard template boards**; each image carries 3–5 programmatically generated defects of a single class. The upstream release provides COCO annotations and a split whose two halves **share the same boards**; this release fixes the packaging and adds a **board-disjoint split** that matches the split used in the literature. > ⚠️ **Upstream license: none stated.** The original authors have never declared a license for > this dataset. See [License](#license) — verify before commercial use. ### Corrections vs. the official release | # | Correction | Detail | |---|---|---| | 1 | **Added YOLO + VOC formats** | Upstream ships COCO JSON only. This release adds Pascal VOC XML and YOLO TXT (all three verified to agree). | | 2 | **Added a board-disjoint split** | The upstream `train` / `val` COCO files put **all 10 boards in both halves** (100 % board-level leakage — see below). This release also ships `train_board.txt` / `val_board.txt` (541 / 152), which matches the split sizes used in the literature. | | 3 | **Packaging** | `classes.txt`, `data.yaml`, `ImageSets/` added. | **No bounding box was modified and every image is byte-identical to the upstream file.** ### ⚠️ The board-level leakage in the upstream split Every image is a render of one of **10 base boards** with different synthetic defects. The upstream split distributes *images* randomly, so **each of the 10 boards appears in both the train and the val half**: | | boards present | |---|---| | upstream `train.json` (555 images) | 01, 04, 05, 06, 07, 08, 09, 10, 11, 12 | | upstream `val.json` (138 images) | 01, 04, 05, 06, 07, 08, 09, 10, 11, 12 | A model evaluated on that val half sees boards it has already memorized, which inflates detection scores. This release therefore **also** provides a board-disjoint split: | split | boards | images | |---|---|---| | `ImageSets/train_board.txt` | 01, 04, 05, 06, 07, 08, 09 | **541** | | `ImageSets/val_board.txt` | 10, 11, 12 | **152** | `data.yaml` points at the board-disjoint split by default; the upstream split is preserved in `ImageSets/train.txt` / `val.txt` for comparability. ### Not a defect — intentional by design Images from the same board are **99.99 % identical** (the defects are a few dozen pixels). A naive duplicate scan reports ~27,000 near-identical pairs — **these are the dataset's design** (same board + different synthetic defects), not duplicates, and they are left untouched. ### Dataset at a glance | Property | Value | |---|---| | Images | **693** | | Classes | **6** — `missing_hole`, `mouse_bite`, `open_circuit`, `short`, `spur`, `spurious_copper` | | Bounding boxes | **2,953** (3–5 per image) | | Base boards | **10** | | Image size | 2240 × 2016 … 3056 × 2464 (10 distinct sizes, one per board) | | Formats | COCO JSON · Pascal VOC XML · YOLO TXT | | Split | board-disjoint **541 / 152** · upstream **555 / 138** | **Boxes per class:** `spurious_copper` 503 · `missing_hole` 497 · `mouse_bite` 492 · `short` 491 · `spur` 488 · `open_circuit` 482 > The upstream release also contains **693 rotated copies** (1,386 images in total) for a > registration task. **This release covers the 693 upright images only.** ### Structure ``` PKU-Market-PCB-corrected/ ├── images/ # 693 jpg ├── labels/ # YOLO: cls cx cy w h (normalized) ├── annotations/ # Pascal VOC XML ├── annotations_raw/ # upstream COCO: train.json, val.json ├── ImageSets/ │ ├── train_board.txt # 541 (boards 01,04,05,06,07,08,09) ← recommended │ ├── val_board.txt # 152 (boards 10,11,12) │ ├── train.txt # 555 (upstream split — has board leakage) │ └── val.txt # 138 (upstream split — has board leakage) ├── classes.txt ├── data.yaml ├── LICENSE └── README.md ``` `labels/`, `annotations/` and `annotations_raw/` are three representations of the **same** annotations and are verified to agree. Class IDs are `0..5` throughout. ### Citation **1. The original dataset — please always cite this.** ```bibtex @misc{huang2019pcb, title = {A PCB Dataset for Defects Detection and Classification}, author = {Huang, Weibo and Wei, Peng}, year = {2019}, eprint = {1901.08204}, archivePrefix = {arXiv}, primaryClass = {cs.CV} } ``` **2. This corrected release — please cite it as well.** It is not identical to the official release: YOLO and VOC annotations were added, and a board-disjoint split was added. ```bibtex @misc{pku_market_pcb_corrected, author = {KeenForgeAI}, title = {PKU-Market-PCB-corrected: a cleaned release of the PKU-Market-PCB PCB-defect dataset}, year = {2026}, version = {1.0}, publisher = {KeenForgeAI}, url = {https://huggingface.co/datasets/KeenForgeAI/PKU-Market-PCB-corrected}, note = {Curated by Lu Gan and Sam Li. Derived from Huang & Wei (2019), arXiv:1901.08204. Upstream licence unstated.} } ``` **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 **The upstream PKU-Market-PCB dataset states no license.** The dataset is offered for download by the Open Lab on Human Robot Interaction (Peking University) and the associated paper is on arXiv; neither grants an explicit license for the data. The copyright status of the images and annotations is therefore **undetermined**. - **The modifications in this release** (format conversion, split, packaging, documentation) are released under **CC BY 4.0**. - **The underlying images and annotations** remain under whatever terms the original authors intend. **Verify the upstream license before any commercial or redistributive use.** See [`LICENSE`](LICENSE). --- ## 中文 ### 这是什么？ **PKU-Market-PCB** 数据集（Huang, Weibo & Wei, Peng，*"A PCB Dataset for Defects Detection and Classification"*，[arXiv:1901.08204](https://arxiv.org/abs/1901.08204)，2019）的**清理与规范化 打包版**。该数据集由**北京大学 人机交互机器人开放实验室**发布。 图像由 **10 块标准模板板**合成，每张含 3–5 个程序生成的同类缺陷。上游提供 COCO 标注，但其 划分的两半**共享同一批底板**；本版修复打包问题，并补上**底板不重叠划分**（与文献使用的划分一致）。 > ⚠️ **上游未声明许可证。** 原作者从未为本数据集声明许可证。详见[许可证](#许可证)——商用前请自行确认。 ### 相对官方版的修正 | # | 修正 | 说明 | |---|---|---| | 1 | **补充 YOLO + VOC** | 上游仅 COCO JSON；本版新增 Pascal VOC XML 与 YOLO TXT（三种格式已校验一致）。 | | 2 | **补底板不重叠划分** | 上游 `train`/`val` 把 **10 块板全部放进两边**（100% 底板级泄漏，见下）。本版另提供 `train_board.txt`/`val_board.txt`（541/152），与文献使用的划分规模一致。 | | 3 | **规范化打包** | 新增 `classes.txt`、`data.yaml`、`ImageSets/`。 | **未改动任何标注框；每张图与上游文件逐字节一致。** ### ⚠️ 上游划分的底板级泄漏 每张图都是 **10 块底板之一** + 不同合成缺陷。上游按图片随机划分，导致**10 块板全部同时出现在 train 和 val 两边**： | | 包含的底板 | |---|---| | 上游 `train.json`（555 图） | 01, 04, 05, 06, 07, 08, 09, 10, 11, 12 | | 上游 `val.json`（138 图） | 01, 04, 05, 06, 07, 08, 09, 10, 11, 12 | 模型在这个 val 上评测时，看到的都是**已经背下来的板子**，检测分数会被虚高。因此本版**另外**提供 底板不重叠划分： | 划分 | 底板 | 图片数 | |---|---|---| | `ImageSets/train_board.txt` | 01, 04, 05, 06, 07, 08, 09 | **541** | | `ImageSets/val_board.txt` | 10, 11, 12 | **152** | `data.yaml` 默认指向底板不重叠划分；上游划分保留在 `ImageSets/train.txt`/`val.txt` 以便对照。 ### 不是缺陷 —— 设计使然 同一块板的图像之间**99.99% 相同**（缺陷只有几十个像素）。天真的去重扫描会报出约 27,000 对近似图 —— **这是数据集的设计**（同板 + 不同合成缺陷），不是重复，本版一律保留。 ### 数据集概览 | 属性 | 值 | |---|---| | 图片 | **693** | | 类别 | **6** —— `missing_hole`、`mouse_bite`、`open_circuit`、`short`、`spur`、`spurious_copper` | | 标注框 | **2,953**（每图 3–5 个） | | 底板 | **10 块** | | 图像尺寸 | 2240×2016 … 3056×2464（10 种，每种对应一块板） | | 格式 | COCO JSON · Pascal VOC XML · YOLO TXT | | 划分 | 底板不重叠 **541 / 152** · 上游 **555 / 138** | **各类框数**：`spurious_copper` 503 · `missing_hole` 497 · `mouse_bite` 492 · `short` 491 · `spur` 488 · `open_circuit` 482 > 上游还包含 **693 张旋转副本**（共 1,386 张）用于配准任务。**本版仅含 693 张正向图。** ### 目录结构 ``` PKU-Market-PCB-corrected/ ├── images/ # 693 张 jpg ├── labels/ # YOLO：cls cx cy w h（归一化） ├── annotations/ # Pascal VOC XML ├── annotations_raw/ # 上游 COCO：train.json、val.json ├── ImageSets/ │ ├── train_board.txt # 541（底板 01,04,05,06,07,08,09）← 推荐 │ ├── val_board.txt # 152（底板 10,11,12） │ ├── train.txt # 555（上游划分 —— 有底板泄漏） │ └── val.txt # 138（上游划分 —— 有底板泄漏） ├── classes.txt ├── data.yaml ├── LICENSE └── README.md ``` `labels/`、`annotations/`、`annotations_raw/` 是**同一份**标注的三种表示，已校验一致。类 ID 统一为 `0..5`。 ### 引用 **1. 原始数据集（请务必引用）** —— 见上方英文部分 `huang2019pcb`。 **2. 本修正版（请一并引用）** —— 本版与官方发布并不相同：补充了 YOLO 与 VOC 标注、 新增了底板不重叠划分。见上方 `pku_market_pcb_corrected`。 **3. 标注工具（可选）** —— 见上方 `keenforge`。 ### 许可证 **上游 PKU-Market-PCB 未声明许可证。** 数据集由北京大学人机交互机器人开放实验室提供下载，相关论文 挂在 arXiv；两者都未给出明确的数据许可。因此图像与标注的**版权状态未定**。 - **本版的修改部分**（格式转换、划分、打包、文档）以 **CC BY 4.0** 发布。 - **底层图像与标注**仍归原作者所声明的条款（目前未声明）。**商用或再分发前请自行确认上游许可。** 详见 [`LICENSE`](LICENSE)。