Robust Fabric Defect Classification Using Deep Learning with Grad-CAM Explainability

Most fabric-defect papers report accuracy from one train-test split, so there’s no way to know if it holds on a different slice of data. This work pairs five-fold stratified cross-validation with a fully separate held-out test set across five CNN architectures under identical conditions, something none of the compared prior work does. EfficientNet-B3’s 98.26% test accuracy tracked its cross-validation estimate closely, not a lucky split. Grad-CAM visualizations suggest the model attends to actual defect regions rather than background shortcuts, feeding into a practical 0.85-confidence rule for routing uncertain cases to human review.