MultitaskPainting100k数据集 用于艺术风格AIGC生成

The dataset used for the evaluation of our multitask deep multibranch neural network has been obtained from the Painter by Numbers Kaggle competition (link). However the original split is not suitable for our task. To accomplish our task we select a subset of the original dataset such that there are at least 10 images in every class for a total of 1508 artists, 125 styles and 41 genres. We call this selection the MultitaskPainting100k dataset. The dataset is split in two parts: a random 70% belonging to the train set and the remaining 30% to the test set. Paintings from the MultitaskPaintings100k dataset. Each row contains samples from a different artist. For each artist we show paintings with different genres and styles. Color coding is used to distinguish between genres and styles. Distributions of number of samples available for each of the 1508 artists, 41 genres and 125 styles within theMultitaskPaintings100k dataset. The names of classes are partially shown for lack of space.

本研究用于评估其所提出的多任务深度多分支神经网络的数据集，源自Kaggle平台的Painter by Numbers竞赛（链接未提供）。然而原始数据集的划分方式无法适配本研究的任务需求。为此，我们从原始数据集中筛选出满足每个类别至少包含10幅图像要求的子集，最终得到涵盖1508位艺术家、125种绘画风格与41种绘画题材的数据集，将其命名为多任务绘画100k（MultitaskPainting100k）数据集。
该数据集被划分为两个子集：随机抽取70%作为训练集，剩余30%作为测试集。多任务绘画100k（MultitaskPainting100k）数据集包含多幅绘画作品，数据集中每一行对应一位不同艺术家的样本。针对每位艺术家，我们展示其创作的不同题材与风格的绘画作品，并通过颜色编码区分不同的绘画题材与风格。
本数据集还展示了1508位艺术家、41种题材及125种风格各自对应的样本数量分布。受篇幅限制，仅部分类别名称予以展示。

搜集汇总

背景与挑战

背景概述

MultitaskPainting100k数据集是一个用于艺术风格AIGC生成的数据集，包含1508位艺术家、125种风格和41种流派的艺术作品，总大小为51.2G。数据集来源于Painter by Numbers Kaggle比赛，经过特定子集选择后分为70%训练集和30%测试集，适用于多任务深度多分支神经网络的评估。