2D geometric shapes dataset

This dataset is composed of 2D 9 geometric shapes, each shape is drawn randomly on a 200x200 RGB image. During the generation of this dataset, the perimeter and the position of each shape are selected randomly and independently for each image, the rotation angle of each shape is selected randomly for each image within an interval between -180° and 180°, as well the background colour of each image and the filling colour of each shape is selected randomly and independently. The published dataset is composed of 9 data classes, each class represent a type of geometric shape (Triangle, Square, Pentagon, Hexagon, Heptagon, Octagon, Nonagon, Circle and Star). Each class is composed of 10k generated image. This paper includes also a GitHub URL to the generator source code used for the generation which can be reused to generate any desired size of data. The proposed dataset aims to provide a perfectly clean dataset, for classification as well clustering purposes, the fact that this dataset is generated synthetically provides the ability to use it to study the behaviour of machine learning models independently of the nature of the dataset or the possible noise or data leak that can be found in any other datasets. Moreover, the choice of a 2D geometrical shape dataset provides the ability to understand as well to have good knowledge of the number of patterns stored inside each data class.本数据集由9种二维几何图形构成，每种图形均随机绘制于200×200的RGB图像之上。在数据集生成过程中，每张图像内各图形的周长与位置均为随机独立选取；每个图形的旋转角度也会在-180°至180°的区间内随每张图像随机确定。此外，每张图像的背景颜色与各图形的填充颜色同样为随机独立选取。 本次发布的数据集共包含9个数据类别，每个类别对应一种几何图形类型，分别为三角形（Triangle）、正方形（Square）、五边形（Pentagon）、六边形（Hexagon）、七边形（Heptagon）、八边形（Octagon）、九边形（Nonagon）、圆形（Circle）与星形（Star）。每个类别包含10000张生成图像。 本论文还附带了用于生成该数据集的源代码GitHub链接，使用者可复用该代码生成任意规模的数据集。 本数据集旨在提供一套纯净无噪的标准数据集，可用于分类与聚类任务。由于该数据集为合成生成，研究者可借助它独立研究机器学习模型的行为，无需考虑其他数据集固有的数据特性、潜在噪声或数据泄露问题。 此外，选用二维几何图形作为数据集样本，便于研究者理解并精准掌握每个数据类别所涵盖的模式数量。