CID-20: Comprehensive Industrial Defects Dataset for Few-Shot Segmentation

CID-20 (Comprehensive Industrial Defects-20) is a benchmark dataset for few-shot industrial defect segmentation, containing 20,344 images across 20 product categories with pixel-level annotations. This dataset combines and extends samples from Industrial-5i (Shi et al., 2023) and Real-IAD (Wang et al., 2024) datasets to provide diverse real-world defect patterns for evaluating automated quality control systems. **Dataset Composition:** - 20,344 total images (15,710 normal, 4,634 defective) - 20 product categories across metals, plastics, fabrics, and ceramics - Pixel-level segmentation masks for all defective samples - 4-fold cross-validation splits for rigorous evaluation - Multiple defect types: scratches, dents, contamination, misalignment, color variations **Key Features:** - Balanced representation of subtle and obvious defects - Varying defect sizes (0.1% to 10%+ of image area) - Realistic imaging conditions with varying lighting and backgrounds - Designed for few-shot learning scenarios with minimal annotation requirements **Attribution:** This dataset builds upon: - Industrial-5i dataset (Shi et al., 2023) - CC BY-NC-SA 4.0 - Real-IAD dataset (Wang et al., 2024) - CC BY-NC-SA 4.0 **License:** CC BY-NC-SA 4.0 (Non-commercial use only) **Original Datasets:** - Industrial-5i: [https://www.scidb.cn/en/anonymous/YWlBMzJ5] - Real-IAD: [https://realiad4ad.github.io/Real-IAD/]