2026-10-15T11:52:02Z https://data.mendeley.com/oai

oai:data.mendeley.com/xj7czzzzyx.1 2026-06-30T04:33:57Z

郭, 海璐 An Image Dataset for Appearance Defect Detection of Cigarette Packets Mendeley Data Dataset Annotation Format and Defect Category Description All annotation files of the Cigarette-Packet-Defect-Dataset-v1.0 are stored in the standard YOLO TXT format. Each line in an annotation file corresponds to one defective object instance in the image, with the format: `class_id normalized_center_x normalized_center_y normalized_width normalized_height` All coordinate parameters are normalized to the range [0, 1], which can be directly used for training YOLO-series object detection models. The dataset contains 9 types of appearance defects for cigarette packets. The correspondence between class IDs, English identifiers, and defect descriptions is as follows: - 0 – baoke (Logo paper cracking) - 1 – zhizhou (Logo paper wrinkling) - 2 – qiaobian (Logo paper edge warping) - 3 – zhiwu (Logo paper staining) - 4 – jiaza (Foreign matter on packet surface) - 5 – changduanke (Logo paper length mismatch) - 6 – zhapo (Packet surface crushing) - 7 – chujiao (Transparent film corner protrusion) - 8 – zhipo (Transparent film tearing) The annotation error of all object bounding boxes is controlled within 5 pixels, and the annotation accuracy meets the training requirements of industrial vision inspection models. Regarding file naming and matching rules, the original image files and corresponding annotation files adopt a same-name association mechanism: image filenames can directly contain defect class IDs or class names (such as `0_001.jpg`, `chujiao_052.png`, `baoke_test_103.jpg`, etc.), and image files with the same ID/name are paired with TXT annotation files. Image Database Liu, Tao Yang, Xinan Zhang, Bo Zhu, Wenkui Dataset doi:10.17632/xj7czzzzyx.1 oai:data.mendeley.com/xj7czzzzyx.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/xj7czzzzyx 2026-06-30T04:33:57Z 2026-09-30T23:59:59Z