Surface defects in copper strips poses quality and performance risks. Classifying defects enables for the rapid identification and removal of the causes of their occurrence, as well as the provision of appropriate treatment to fix them. This Deep Learning-based solution identifies four classes of salient surface defects: pits, holes, burrs, and scratches. This solution analyses the user provided image data, identifies the best performing deep learning model architecture, and predicts the defect class with the highest probability score. This can assist metal products manufacturing companies to improve their quality control process.