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: Sphere: 0, Cube: 1, Cone: 2, Cylinder: 3, Ellipsoid: 4, Cuboid: 5, Pyramid: 6 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（Voxel）数据集是一套面向机器学习（ML）与神经架构搜索（NAS）工具构建的三维形状数据集，旨在为其设置独特的任务挑战。与常规三维物体图像数据集不同，本数据集利用机器学习可在多“颜色”通道上进行运算的特性，将通道维度转化为图像的第三维空间维度。本数据集是2024年神经架构搜索不可见数据挑战赛（2024 NAS Unseen-Data Challenge）所采用的三大隐藏数据集之一。本数据集包含70000张由七种不同三维形状生成的三维图像，其构建流程为：在三维空间中搭建20×20×20的点网格，随机生成各类三维形状（详见下文），并记录该形状与网格点的碰撞情况，从而生成数据集内类体素的三维形状数据。数据集的数据维度为(n, 20, 20, 20)，其中n代表对应子集的样本量：训练集含50000个样本，验证集与测试集各含10000个样本。针对每一类（即每一种三维形状），我们均生成了10000个样本，并将其均匀分配至上述三个子集当中。各类别及其对应的数字标签如下：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，只需将文件路径作为参数传入`numpy.load()`函数即可。元数据文件包含本数据集的部分基础信息，可通过Vim、Nano、Notepad++、记事本等多款文本编辑器打开。