KeenForgeAI/PCB_AoI_KubeEdge-corrected

PCBAoIKubeEdge-corrected


PCB automated optical inspection (AOI) defect detection — cleaned version of the KubeEdge-Ianvs PCB-AoI public dataset. PCB 自动光学检测（AOI）缺陷检测 —— KubeEdge-Ianvs PCB-AoI 公开数据集的清理修正版。

English · 中文

English

What is this?

A cleaned, de-duplicated and consistently packaged version of the PCB-AoI Public Dataset, released by KubeEdge SIG AI members from China Telecom and Raisecom Technology as an industrial defect-detection scenario of the KubeEdge-Ianvs distributed synergy AI benchmarking project.

The upstream release ships as a flat, unsplit train_data/ + test_data/ + train_data_augmentation/ trio (Pascal VOC), where the augmentation folder silently mixes the original tiles with their geometric transforms. This release keeps every image pixel unchanged, separates originals from augmentations, removes a leakage-prone slice of the augmentation set, and packages the data in both Pascal VOC and YOLO format with a reproducible train/val/test split.

⚠️ Upstream license: none stated. PCB-AoI ships no LICENSE file and no terms of use. See License & attribution — verify before commercial use.

Corrections vs. the official release

Image pixels are never modified. All 1,115 image files (233 core + 882 augmented) are byte-for-byte identical to the upstream files.

Scope — structural clean-up only. This is not a re-annotation. We separate, de-duplicate and re-package the data; we do not re-verify individual boxes (missed defects, wrong class labels, box tightness) — that requires domain expertise and is left untouched.

Structural checks performed on the released set:

Dataset at a glance

Core split (recommended evaluation)

Boxes per class (core): Bad_podu 830 · Bad_qiaojiao 181

Boxes per split: train 604 (Bad_podu 477 / Bad_qiaojiao 127) · val 75 (58 / 17) · test 332 (295 / 37)

Augmented set (training-only)

Boxes per class (augmented): Bad_podu 2,862 · Bad_qiaojiao 762

Structure

PCB_AoI_KubeEdge-corrected/
├── images/{train,val,test}/      # 600x600 RGB .jpeg (core split)
├── labels/{train,val,test}/      # YOLO: cls cx cy w h (normalized)
├── Annotations/                  # Pascal VOC XML (flat, cleaned)
├── JPEGImages/                   # copy of the core images (VOC layout)
├── augmented/
│   ├── images/                   # 882 training-only transforms
│   ├── labels/                   # YOLO labels for the augmented set
│   ├── Annotations/              # Pascal VOC XML for the augmented set
│   └── data.yaml                 # train-only config (originals excluded)
├── classes.txt
├── data.yaml                     # core: images/{train,val,test}
├── data_with_augment.yaml        # core train + augmented (recommended recipe)
├── LICENSE
└── README.md

Annotations/**/*.xml and labels/**/*.txt are two representations of the same cleaned annotations and are verified to agree. data.yaml is the clean evaluation recipe; data_with_augment.yaml is the recommended training recipe (core train + augmented).

Quick start

from ultralytics import YOLO

model = YOLO("yolov8n.pt")
model.train(data="data_with_augment.yaml", epochs=100, imgsz=640)  # train with augmentation
model.val(data="data.yaml")                                        # evaluate on clean val/test

Citation

1. The original dataset — please always cite this.

@misc{pcbaoikubeedge,
  title        = {PCB-AoI Public Dataset},
  author       = {{KubeEdge SIG AI} and {China Telecom} and {Raisecom Technology}},
  year         = {2021},
  howpublished = {KubeEdge-Ianvs distributed synergy AI benchmarking project},
  url          = {https://www.kaggle.com/datasets/kubeedgeianvs/pcb-aoi},
  note         = {Industrial defect-detection scenario of the Ianvs benchmark}
}

2. This corrected release — please cite it as well. It is not identical to the official release: the augmentation set was separated and leakage-pruned, a reproducible split was added, and the data was re-packaged, so a citation to the original alone does not describe this version.

@misc{pcb_aoi_kubeedge_corrected,
  author    = {KeenForgeAI},
  title     = {PCB_AoI_KubeEdge-corrected: a cleaned release of the KubeEdge-Ianvs PCB-AoI dataset},
  year      = {2026},
  version   = {1.0},
  publisher = {KeenForgeAI},
  url       = {https://huggingface.co/datasets/KeenForgeAI/PCB_AoI_KubeEdge-corrected},
  note      = {Curated by Lu Gan and Sam Li. Derived from the KubeEdge-Ianvs PCB-AoI
               Public Dataset (China Telecom / Raisecom Technology). Upstream licence unstated.}
}

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

The upstream PCB-AoI dataset states no license. The archive ships no LICENSE file and no terms of use, and the KubeEdge-Ianvs project license covers the tooling, not the image data. The copyright status of the original images and annotations is therefore undetermined — verify it before any commercial use. See LICENSE in this repository.

Our modifications (separation, de-duplication, split, format conversion, documentation) are released under CC BY 4.0. Image pixels are unmodified and remain subject to the upstream terms.

Provenance

• —Source: KubeEdge-Ianvs PCB-AoI public dataset (Kaggle kubeedgeianvs/pcb-aoi / KubeEdge OBS), retrieved 2026-09-29.
• —Upstream: train_data 173 img / 679 box · test_data 60 img / 332 box · train_data_augmentation 1,211 img / 4,753 box (173 originals + 1,038 transforms).
• —Verified: 233 core images (1,011 boxes, byte-identical) + 882 training-only transforms (3,624 boxes); 0 leakage, 0 invalid boxes.

中文

这是什么？

PCB-AoI 公开数据集的清理、去重、规范化打包版。该数据集由 中国电信 与 瑞斯康达 （Raisecom Technology） 的 KubeEdge SIG AI 成员，作为 KubeEdge-Ianvs 分布式协同 AI 基准测试的工业缺陷检测 场景发布。

上游以扁平的、无划分的 train_data/ + test_data/ + train_data_augmentation/ 三目录 （Pascal VOC）发布，其中增强目录把原始图与几何变换图混在一起。本版不修改任何图像像素， 将原始图与增强图分离、剔除易泄漏的增强切片，并以 Pascal VOC + YOLO 双格式打包， 附带可复现的 train/val/test 划分。

⚠️ 上游未声明许可证。 PCB-AoI 未附带 LICENSE 文件，也无使用条款。详见 许可证与署名——商用前请自行确认。

相对官方版的修正

图像像素从不修改——全部 1,115 张图（233 核心 + 882 增强）与上游文件逐字节一致。

本版范围——仅结构化清理。 这是结构性清理，不是重新标注。我们只做分离、去重、重新打包； 不重新核对单个标注框（漏标、类别标错、框松紧）——那需要领域专业知识，一律保持原样。

已执行的结构化检查（针对发布的集合）：

数据集概览

核心划分（推荐用于评估）

各类框数（核心）：Bad_podu 830 · Bad_qiaojiao 181

各划分框数：train 604（Bad_podu 477 / Bad_qiaojiao 127）· val 75（58 / 17）· test 332（295 / 37）

增强集（仅用于训练）

各类框数（增强）：Bad_podu 2,862 · Bad_qiaojiao 762

目录结构

PCB_AoI_KubeEdge-corrected/
├── images/{train,val,test}/      # 600x600 RGB .jpeg（核心划分）
├── labels/{train,val,test}/      # YOLO：cls cx cy w h（归一化）
├── Annotations/                  # Pascal VOC XML（扁平，已清理）
├── JPEGImages/                   # 核心图片副本（VOC 布局）
├── augmented/
│   ├── images/                   # 882 张仅训练用的变换图
│   ├── labels/                   # 增强集的 YOLO 标签
│   ├── Annotations/              # 增强集的 Pascal VOC XML
│   └── data.yaml                 # 仅训练配置（不含原始图）
├── classes.txt
├── data.yaml                     # 核心：images/{train,val,test}
├── data_with_augment.yaml        # 核心训练 + 增强（推荐配方）
├── LICENSE
└── README.md

Annotations/**/*.xml 与 labels/**/*.txt 是同一份清理后标注的两种表示，已校验一致。 data.yaml 为干净的评估配方；data_with_augment.yaml 为推荐的训练配方（核心训练 + 增强）。

快速开始

from ultralytics import YOLO

model = YOLO("yolov8n.pt")
model.train(data="data_with_augment.yaml", epochs=100, imgsz=640)  # 带增强训练
model.val(data="data.yaml")                                        # 在干净的 val/test 上评估

引用

使用本数据集请同时引用原始工作与本修正版：

1. 原始数据集（请务必引用）

@misc{pcbaoikubeedge,
  title        = {PCB-AoI Public Dataset},
  author       = {{KubeEdge SIG AI} and {China Telecom} and {Raisecom Technology}},
  year         = {2021},
  howpublished = {KubeEdge-Ianvs distributed synergy AI benchmarking project},
  url          = {https://www.kaggle.com/datasets/kubeedgeianvs/pcb-aoi},
  note         = {Industrial defect-detection scenario of the Ianvs benchmark}
}

2. 本修正版（请一并引用） —— 本版与官方发布并不相同：分离并对增强集做了防泄漏处理， 补充了可复现划分，并重新打包，因此只引用原数据集无法描述本版本。

@misc{pcb_aoi_kubeedge_corrected,
  author    = {KeenForgeAI},
  title     = {PCB_AoI_KubeEdge-corrected: a cleaned release of the KubeEdge-Ianvs PCB-AoI dataset},
  year      = {2026},
  version   = {1.0},
  publisher = {KeenForgeAI},
  url       = {https://huggingface.co/datasets/KeenForgeAI/PCB_AoI_KubeEdge-corrected},
  note      = {Curated by Lu Gan and Sam Li. Derived from the KubeEdge-Ianvs PCB-AoI
               Public Dataset (China Telecom / Raisecom Technology). Upstream licence unstated.}
}

3. 标注工具（可选）

@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.}
}

许可证与署名

上游 PCB-AoI 数据集未声明许可证。 压缩包内无 LICENSE 文件、无使用条款，且 KubeEdge-Ianvs 项目许可证覆盖的是工具而非图像数据。因此原始图像与标注的版权状态未定——商用前请自行确认。 详见本仓库 LICENSE。

我们对数据集的修改部分（分离、去重、划分、格式转换、文档）以 CC BY 4.0 发布。 图像像素未修改，仍受上游条款约束。

来源

• —来源：KubeEdge-Ianvs PCB-AoI 公开数据集（Kaggle kubeedgeianvs/pcb-aoi / KubeEdge OBS），获取于 2026-09-29
• —上游：train_data 173 图 / 679 框 · test_data 60 图 / 332 框 · train_data_augmentation 1,211 图 / 4,753 框（173 原始图 + 1,038 变换图）
• —已校验：233 核心图（1,011 框，逐字节一致）+ 882 仅训练变换图（3,624 框）；0 泄漏、0 非法框