Sc Net

(1) Purpose: A weakly supervised surface defect detection model using image-level labels for simultaneous classification and segmentation. (2) Experiments: run on 4 datasets, including KolektorSDD2(KSDD2), DAGM 1-10, KolektorSDD(KSDD), Severstal Steel, the classification average precision (AP) reaches 96.0%, 100%, 97.1%, 97.7%,respectively. (1)用途：一种使用图像级标签的弱监督表面缺陷检测模型，可同时进行分类和分割。 (2)实验：在4个数据集上实验，包括KolektorSDD2(KSDD2), DAGM 1-10, KolektorSDD(KSDD), Severstal Steel，分类平均精确率(AP)分别达到96.0%, 100%, 97.1%, 97.7%。