Datasets play a pivotal role in training visual models, facilitating the development of abstract understandings of visual features through diverse image samples and multidimensional attributes. However, in the realm of aesthetic evaluation of artistic images, datasets remain relatively scarce. Existing painting datasets are often characterized by limited scoring dimensions and insufficient annotations, thereby constraining the advancement and application of automatic aesthetic evaluation methods in the domain of painting. To bridge this gap, we introduce the Aesthetics Paintings and Drawings Dataset (APDD), the first comprehensive collection of paintings encompassing 24 distinct artistic categories and 10 aesthetic attributes. Building upon the initial release of APDDv1, our ongoing research has identified opportunities for enhancement in data scale and annotation precision. Consequently, APDDv2 boasts an expanded image corpus and improved annotation quality, featuring detailed language comments to better cater to the needs of both researchers and practitioners seeking high-quality painting datasets. Furthermore, we present an updated version of the Art Assessment Network for Specific Painting Styles, denoted as ArtCLIP. Experimental validation demonstrates the superior performance of this revised model in the realm of aesthetic evaluation, surpassing its predecessor in accuracy and efficacy. The dataset and model are available at https://github.com/BestiVictory/APDDv2.git.

翻译：数据集在训练视觉模型中扮演着关键角色，通过多样化的图像样本和多维属性，促进对视觉特征的抽象理解。然而，在艺术图像的美学评估领域，数据集仍然相对匮乏。现有的绘画数据集通常存在评分维度有限和标注不足的问题，从而制约了自动美学评估方法在绘画领域的发展与应用。为弥补这一空白，我们推出了绘画美学数据集（APDD），这是首个涵盖24种不同艺术类别和10种美学属性的综合性绘画集合。在APDDv1初始发布的基础上，我们的持续研究发现了在数据规模和标注精度方面的改进空间。因此，APDDv2拥有扩展的图像库和提升的标注质量，并包含详细的语言评论，以更好地满足寻求高质量绘画数据集的研究人员和从业者的需求。此外，我们提出了特定绘画风格艺术评估网络的更新版本，命名为ArtCLIP。实验验证表明，该修订模型在美学评估领域表现出优越性能，在准确性和有效性上超越了其前身。数据集和模型可在 https://github.com/BestiVictory/APDDv2.git 获取。