KeenForgeAI/DeepPCB-corrected

DeepPCB-corrected


PCB defect detection — cleaned version of the DeepPCB dataset. PCB 缺陷检测 —— DeepPCB 数据集的清理修正版。

English · 中文

English

What is this?

A cleaned and consistently packaged version of the DeepPCB dataset (Tang, Mao, Wang, Zhu & Ye — "A Robust PCB Defect Detection Method via Fusing Multiple Hierarchical Features", arXiv:1902.06197).

DeepPCB pairs each defect-free template image with an aligned tested image carrying bounding-box annotations for six common PCB defects. The upstream release ships a custom TXT format and a split list that does not resolve; this release fixes the packaging, removes the few broken samples, and adds the two formats the tool ecosystem expects.

✅ Upstream license: MIT (© 2018 tangsanli5201). This release keeps the same license.

Corrections vs. the official release

Boxes and image content are otherwise untouched. Every retained image is byte-identical to the upstream file (except the 61 RGB→grayscale re-encodings, which do not change content).

Not a defect — intentional by design

The dataset reuses templates and gives each tested image synthetic defects: the README states "we manually argument some artificial defects on each tested image". Consequently a tested image is almost identical to its template (and templates recur across images). A naive duplicate scan will report ~350 near-identical pairs — these are the dataset's design, not errors, and they are left untouched.

Dataset at a glance

Boxes per class: mousebite 1,963 · open 1,940 · spur 1,624 · short 1,504 · pin-hole 1,500 · copper 1,473

Upstream class labels (per its README) are open, short, mousebite, spur, copper, pin-hole. Some third-party re-exports rename the last two to spurious_copper / pin_hole.

Structure

DeepPCB-corrected/
├── images/            # 1499 tested (defective) images: <stem>_test.jpg
├── templates/         # 1499 defect-free templates:    <stem>_temp.jpg
├── labels/            # YOLO: cls cx cy w h (normalized)
├── annotations/       # Pascal VOC XML
├── annotations_raw/   # original upstream format: x1 y1 x2 y2 type
├── ImageSets/
│   ├── trainval.txt   # 1000 stems
│   └── test.txt       #  499 stems
├── classes.txt
├── data.yaml          # YOLO dataset config
├── LICENSE
└── README.md

labels/, annotations/ and annotations_raw/ are three representations of the same annotations and are verified to agree. Class IDs are 1..6 in annotations_raw (upstream convention) and 0..5 in YOLO/VOC.

Citation

1. The original dataset — please always cite this.

@article{tang2019deeppcb,
  title   = {A Robust PCB Defect Detection Method via Fusing Multiple Hierarchical Features},
  author  = {Tang, Sanli and Mao, Fan and Wang, Zhipeng and Zhu, Zhikai and Ye, Shenghua},
  journal = {arXiv preprint arXiv:1902.06197},
  year    = {2019}
}

2. This corrected release — please cite it as well. It is not identical to the official release: the split lists were fixed, one contaminated image and one orphan template were removed, image modes were normalised, and the data was re-packaged.

@misc{deeppcb_corrected,
  author    = {KeenForgeAI},
  title     = {DeepPCB-corrected: a cleaned release of the DeepPCB PCB-defect dataset},
  year      = {2026},
  version   = {1.0},
  publisher = {KeenForgeAI},
  url       = {https://huggingface.co/datasets/KeenForgeAI/DeepPCB-corrected},
  note      = {Curated by Lu Gan and Sam Li. Derived from Tang et al. (2019),
               arXiv:1902.06197. MIT licensed.}
}

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

MIT — the same license as the upstream DeepPCB repository (© 2018 tangsanli5201). See `LICENSE`. Please also credit the original authors.

中文

这是什么？

DeepPCB 数据集（Tang 等，arXiv:1902.06197）的清理与规范化打包版。

DeepPCB 把每张无缺陷模板图与一张对齐后的缺陷图配对，缺陷图带有六类常见 PCB 缺陷的 边界框标注。上游发布的是自定义 TXT 格式，且划分列表无法解析；本版修复打包问题、删除少量 损坏样本，并补上生态常用的两种格式。

✅ 上游许可证：MIT（© 2018 tangsanli5201）。本版沿用同一许可。

相对官方版的修正

除此之外，标注框与图像内容未改动。 保留的每张图与上游文件逐字节一致 （仅上述 61 张 RGB→灰度 的重编码，内容不变）。

不是缺陷 —— 设计使然

数据集刻意复用模板并为每张缺陷图合成缺陷：上游 README 明说 "we manually argument some artificial defects on each tested image"。因此缺陷图与其模板几乎 相同（模板也会跨图复用）。天真的去重扫描会报出约 350 对近似图 —— 这是数据集的设计，不是错误， 本版一律保留。

数据集概览

各类框数：mousebite 1,963 · open 1,940 · spur 1,624 · short 1,504 · pin-hole 1,500 · copper 1,473

官方 README 给出的类别名为 open, short, mousebite, spur, copper, pin-hole。 部分第三方镜像把后两类改名为 spurious_copper / pin_hole。

目录结构

DeepPCB-corrected/
├── images/            # 1499 张缺陷图：<stem>_test.jpg
├── templates/         # 1499 张无缺陷模板：<stem>_temp.jpg
├── labels/            # YOLO：cls cx cy w h（归一化）
├── annotations/       # Pascal VOC XML
├── annotations_raw/   # 上游原格式：x1 y1 x2 y2 type
├── ImageSets/
│   ├── trainval.txt   # 1000 条
│   └── test.txt       #  499 条
├── classes.txt
├── data.yaml          # YOLO 配置
├── 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 在 annotations_raw 中是 1..6（上游约定），在 YOLO/VOC 中是 0..5。

引用

1. 原始数据集（请务必引用） —— 见上方英文部分 tang2019deeppcb。

2. 本修正版（请一并引用） —— 本版与官方发布并不相同：修复了划分列表、删除了 1 张污染图与 1 张孤立模板、统一了图片模式，并重新打包。见上方 deeppcb_corrected。

3. 标注工具（可选） —— 见上方 keenforge。

许可证

MIT —— 与上游 DeepPCB 仓库相同（© 2018 tangsanli5201）。详见 `LICENSE`。 请同时注明原作者。