Voxel Dataset

The Voxel dataset is a constructed dataset of 3D shapes designed to present a unique problem for ML and NAS tools. Instead of a photo of a 3D object, we exploit ML's ability to work across N number of 'colour' channels and use this dimension as a third dimension for images. This dataset is one of the three hidden datasets used by the 2024 NAS Unseen-Data Challenge. The images include 70,000 generated 3D Images of seven different shapes that we generated by creating a 20x20x20 grid of points in 3d space, and randomly generated different 3D shapes (see below) and recorded which of the points the shape collided with, generating the voxel like shapes in the dataset. The data has a shape of (n, 20, 20, 20) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing). For each class (shape), we generated 10,000 samples evenly distributed between the three sets. The three classes and corresponding numerical labels are as follows:<br> Sphere: 0,<br> Cube: 1,<br> Cone: 2,<br> Cylinder: 3,<br> Ellipsoid: 4,<br> Cuboid: 5,<br> Pyramid: 6 <br> NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. The metadata file contains some basic information about the datasets, and can be opened in many text editors such as vim, nano, notepad++, notepad, etc体素数据集（Voxel Dataset）是专为机器学习（Machine Learning，ML）与神经架构搜索（Neural Architecture Search，NAS）工具设计的三维形状构建数据集，旨在为其提供极具针对性的独特研究问题。相较于传统三维物体照片数据集，本数据集利用机器学习对N个“颜色”通道的处理能力，将该通道维度作为图像的第三维空间维度。 本数据集为2024年神经架构搜索未知数据挑战赛（2024 NAS Unseen-Data Challenge）所用的三大隐藏数据集之一。 本数据集包含70000个由七种不同三维形状生成的类体素三维样本：我们通过在三维空间中构建20×20×20的点网格，随机生成各类三维形状（详见下文），并记录形状与网格点的碰撞情况，从而生成本数据集中的类体素形状数据。 数据的张量维度为(n, 20, 20, 20)，其中n为对应子集的样本总量：训练集包含50000个样本，验证集与测试集各含10000个样本。 每个类别（对应一种三维形状）均生成10000个样本，并均匀分配至上述三个子集。七个类别及其对应的数值标签如下： 球体（Sphere）：0 立方体（Cube）：1 圆锥体（Cone）：2 圆柱体（Cylinder）：3 椭球体（Ellipsoid）：4 长方体（Cuboid）：5 棱锥体（Pyramid）：6 可通过Python的NumPy库读取NumPy格式（.npy）文件：调用`numpy.load()`函数并传入文件路径即可完成数据加载。元数据文件包含数据集的基础信息，可通过Vim、Nano、Notepad++、记事本等多款主流文本编辑器打开。