M2 screw defect detection dataset

This dataset contains structural images of M2 screws curated for training and evaluating deep learning-based automated optical inspection (AOI) systems. The collection specifically focuses on isolating anomalies across two distinct regions: the screw Body (thread) and the screw Head. The dataset is divided into structured folders to support binary classification tasks (OK for compliant components and NG for defective components) across both training and testing pipelines. The training data has been strictly class-balanced to mitigate algorithmic optimization bias, while the testing data provides an independent validation benchmark for evaluating real-world system generalization. Total Image Count: 5,536 images Dataset distribution as follows M2_Screw_Dataset/ ├── Body/ │ ├── Train/ │ │ ├── OK/ (1,000 images) │ │ └── NG/ (1,000 images) │ └── Test/ │ ├── OK/ (500 images) │ └── NG/ (500 images) └── Head/ ├── Train/ │ ├── OK/ (1,000 images) │ └── NG/ (1,000 images) └── Test/ ├── OK/ (257 images) └── NG/ (279 images)本数据集包含经筛选整理的M2螺丝结构图像，用于训练与评估基于深度学习的自动光学检测（Automated Optical Inspection, AOI）系统。本数据集的核心聚焦于在两个明确区域内分离异常样本：螺丝杆部（Body）与螺丝头部（Head）。 该数据集采用结构化文件夹架构，可支持针对上述两个区域的二分类任务——合规组件标注为OK，缺陷组件标注为NG，覆盖训练与测试全流程。训练数据严格遵循类别平衡原则，以缓解算法优化偏差；测试数据则作为独立验证基准，用于评估实际场景下的系统泛化能力。 总图像数量为5536张，数据集分布如下： M2_Screw_Dataset/ ├── 杆部（Body）/ │ ├── 训练集（Train）/ │ │ ├── OK/ （1000张图像） │ │ └── NG/ （1000张图像） │ └── 测试集（Test）/ │ ├── OK/ （500张图像） │ └── NG/ （500张图像） └── 头部（Head）/ ├── 训练集（Train）/ │ ├── OK/ （1000张图像） │ └── NG/ （1000张图像） └── 测试集（Test）/ ├── OK/ （257张图像） └── NG/ （279张图像）