The work aims to construct a benchmark dataset for cigarette package appearance defect recognition and carry out research on the application of mainstream deep learning models in the intelligent detection of cigarette package appearance defects, so as to improve the accuracy of cigarette package defect detection. The image data of suspected defects were collected from the normal production ZB45 fine cigarettes hard box packaging machine, and the data with respect to real defects were obtained through manual reviews and screening. According to the characteristics and causes of defects, the defect data were classified into 23 categories, the labels and locations of defect were marked with bounding boxes. A benchmark dataset containing more than 13 000 images of cigarette package appearance quality defects was constructed. Experimental tests were conducted for four tasks, namely, cigarette package defect recognition, defect classification, target detection, and model transfer. The results showed that the dataset fulfilled the training requisites for high-accuracy deep learning models; Through model migration, the dataset could be utilized to significantly improve the accuracy of defect detection for different cigarette grades; The DenseNet model achieved better results on the cigarette packet defect recognition and defect classification tasks, with accuracy rates of 93.70% and 95.43%, respectively, and the YOLOv5 model achieved a mAP@0.5 of 96.61% on the defective target detection task. The dataset can be used as a benchmark dataset in cigarette packet defect detection, and the research results will further support the data application and digital transformation in cigarette packaging.