论文标题
Artbench数据集：用艺术品对生成模型进行基准测试
The ArtBench Dataset: Benchmarking Generative Models with Artworks
论文作者
论文摘要
我们介绍了Artbench-10，这是一流的平衡，高质量的，干净的注释和标准化数据集，用于基准制作艺术品。它包括60,000幅艺术品图像，来自10种独特的艺术风格，每种样式的训练图像和1,000张测试图像。 Artbench-10比以前的艺术品数据集具有多个优势。首先，它是级别平衡的，而大多数以前的艺术品数据集都遭受了较长的尾巴分布。其次，这些图像具有高质量，并带有干净的注释。第三，Artbench-10是通过标准化数据收集，注释，过滤和预处理程序创建的。我们提供了三个具有不同分辨率的数据集（$ 32 \ times32 $，$ 256 \ times256 $和原始图像尺寸），并以一种易于通过流行的机器学习框架进行合并的方式。我们还使用具有ArtBench-10的代表性图像合成模型进行了广泛的基准测试实验，并进行了深入分析。该数据集可在https://github.com/liaopeiyuan/artbench获得公平使用许可证。
We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of artwork from 10 distinctive artistic styles, with 5,000 training images and 1,000 testing images per style. ArtBench-10 has several advantages over previous artwork datasets. Firstly, it is class-balanced while most previous artwork datasets suffer from the long tail class distributions. Secondly, the images are of high quality with clean annotations. Thirdly, ArtBench-10 is created with standardized data collection, annotation, filtering, and preprocessing procedures. We provide three versions of the dataset with different resolutions ($32\times32$, $256\times256$, and original image size), formatted in a way that is easy to be incorporated by popular machine learning frameworks. We also conduct extensive benchmarking experiments using representative image synthesis models with ArtBench-10 and present in-depth analysis. The dataset is available at https://github.com/liaopeiyuan/artbench under a Fair Use license.
