MultNIST Dataset

Dataset containing the images and labels for the MultNIST data used in the CVPR NAS workshop Unseen-data challenge under the codename "Mateo"The MultNIST dataset is a constructed dataset from MNIST Images. The intention of this dataset is to require machine learning models to do more than just image classification but also perform a calculation, in this case multiplaction followed by a mod operation. For each image, three MNIST Images were randomly chosen and combined together through the colour channels, resulting in a three colour-channel image so each MNIST image represents one colour channel. The data is in a channels-first format with a shape of (n, 3, 28, 28) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing).There are ten classes in the dataset, with 7,000 examples of each, distributed evenly between the three subsets.The label of each image is generated using the formula "(r * b * g) % 10" where r, g, and b are the red, green, and blue colour channels respectively. An example of a MultNIST Image would be a rgb configuation of 3, 7, and 4 respectively, which would result in a label of 4 ((3 * 7 * 4) % 10).

本数据集为代号“Mateo”的国际计算机视觉与模式识别会议（Conference on Computer Vision and Pattern Recognition, CVPR）神经架构搜索（Neural Architecture Search, NAS）研讨会“未见数据挑战赛”所用的MultNIST数据提供图像与标签。MultNIST数据集是基于MNIST（Modified National Institute of Standards and Technology）手写数字图像构建的衍生数据集。该数据集的设计初衷并非仅要求机器学习模型完成图像分类任务，而是需同时执行计算操作——此处为先乘法后取模的运算。针对每一张目标图像，会随机选取三张MNIST手写数字图像，并通过色彩通道进行融合，最终得到一张三通道彩色图像，其中每张MNIST图像分别对应一个色彩通道。该数据集采用通道优先（channels-first）格式，数据形状为(n, 3, 28, 28)，其中n为对应子集的样本量：训练集含50000个样本，验证集与测试集各含10000个样本。该数据集共包含10个类别，每个类别含7000个样本，且在训练、验证、测试三个子集内均匀分布。每张图像的标签通过公式“(r * b * g) % 10”生成，其中r、g、b分别代表红色、绿色与蓝色通道的数值。以RGB通道分别为3、7、4的MultNIST图像为例，其对应的标签为4，即(3 × 7 × 4) % 10 = 4。