The CR7-DET dataset consists of surface defect images collected from a steel company in China, representing real-world industrial scenarios. It includes seven types of surface defects: inclusion, dents, oil spots, pits, punching, linear defects, and macular spots. All defects are located on the surface of cold-rolled steel plates. The dataset contains 4140 images, annotated with 11,020 labels. The defect categories are described as follows: Inclusions: Nonmetallic inclusions with spot-like, block-like, or linear surfaces, exhibiting discontinuous or continuous distribution, and appearing reddish brown, dark gray, or white in color. Dents: Straight and fine grooves with varying depths on the surface of the steel plate. Oil Spots: Periodic punctate spots appearing yellow or black on the surface. Pits: Shallow surface depressions resembling gravures. Punching: Irregular and spaced holes penetrating the surface. Linear: Scratches caused by rollers or other structures. Macular: Yellow and white discoloration on the surface, giving a blooming appearance. The dataset is organized into two main subfolders: image/: Contains the actual steel plate images in .jpg format. label/: Contains the corresponding annotation files, such as dents1.txt, which provide detailed information about the defects in the images. These annotations include the defect type, location, and severity, and are annotated using the LabelMe tool. Each annotation file is linked to its corresponding image file, ensuring a one-to-one correspondence between the image and its annotations. This dataset can be used for defect detection tasks, and users can access both the images and annotations for training and testing machine learning models.