Mechanical Parts Dataset 2022
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Mechanical Parts Dataset The dataset consists of a total of 2250 images obtained by downloading from various internet platforms. Among the images in the dataset, there are 714 images with bearings, 632 images with bolts, 616 images with gears and 586 images with nuts. A total of 10597 manual labeling processes were carried out in the dataset, including 2099 labels belonging to the bearing class, 2734 labels belonging to the bolt class, 2662 labels belonging to the gear class and 3102 labels belonging to the nut class. Folder Content The created dataset is divided into 3 as 80% train, 10% validation and 10% test. In the "Mechanical Parts Dataset" folder, there are three separate folders as "train", "test" and "val". In each of these three folders there are folders named "images" and "labels". Images are kept in the "images" folder and tag information is kept in the "labels" folder. Finally, inside the folder there is a yaml file named "mech_parts_data" for the Yolo algorithm. This file contains the number of classes and class names. Images and Labels The dataset was prepared in accordance with the Yolov5 algorithm. For example, the tag information of the image named "2a0xhkr_jpg.rf.45a11bf63c40ad6e47da384fdf6bb7a1.jpg" is stored in the txt file with the same name. The tag information (coordinates) in the txt file are as follows: "class x_center y_center width height". Related paper: doi.org/10.5281/zenodo.7496767
机械零件数据集（Mechanical Parts Dataset）：本数据集共包含2250张从各类互联网平台下载的图像。其中含轴承图像714张、螺栓图像632张、齿轮图像616张、螺母图像586张。本数据集共完成10597次人工标注操作，累计生成10597个标注标签，其中轴承类标签2099个、螺栓类标签2734个、齿轮类标签2662个、螺母类标签3102个。文件夹结构：本数据集按80%训练集、10%验证集、10%测试集的比例划分为三部分。在"Mechanical Parts Dataset"根文件夹下，设有"train"、"test"与"val"三个独立子文件夹。上述三个子文件夹中均包含"images"与"labels"两个目录：图像文件存储于"images"目录，标注信息存储于"labels"目录。此外，该文件夹内还包含一个专为YOLO（You Only Look Once）算法设计的名为"mech_parts_data"的YAML配置文件，该文件包含类别总数与各类别名称。图像与标注规范：本数据集严格遵循Yolov5算法的格式要求制作。例如，名为"2a0xhkr_jpg.rf.45a11bf63c40ad6e47da384fdf6bb7a1.jpg"的图像，其标注信息存储于同名txt文件中。txt文件内的标注信息格式为："class x_center y_center width height"，即"类别 中心点x坐标 中心点y坐标 宽度 高度"。相关论文：doi.org/10.5281/zenodo.7496767
