WISD-AD: Real World Semi Supervised Benchmark for Defect Detection of Soft Magnetic Wound Inductors

Existing public datasets for industrial defect detection generally face two major limitations. Cross-domain benchmarks such as MVTec AD and VisA cover diverse but scattered categories and scenarios, which differ from practical product-level inspection needs, and performance on several commonly used benchmarks has gradually approached saturation. Although domain-specific datasets such as NEU-DET and GC10-DET are closer to real industrial scenarios, their data splits and evaluation protocols are usually inconsistent across studies, hindering fair comparison. More importantly, most existing datasets focus mainly on unsupervised or fully supervised settings, whereas a unified 2D benchmark for semi-supervised settings remains lacking. To address this gap, we propose WISD-AD, a real-world dataset for surface defect detection of soft-magnetic wound inductors. It contains five defect categories with practical challenges such as small defects, weak textures, and class imbalance, and supports fully and semi-supervised classification and detection with unified data splits and evaluation protocols. In addition, we further propose M${}^2$D${}^3$ as a benchmark framework for WISD-AD. Extensive experiments reveal that existing methods still struggle under sparse annotations, and further show that representation quality before self-training critically affects subsequent pseudo-label reliability. M²D³ substantially alleviates this limitation, providing a strong reference for annotation-efficient defect inspection.