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This dataset focuses on the works of a specific artist named Shitao, with its data sourced from Google Arts & Culture's Shitao page (link: https://artsandculture.google.com/entity/shitao/m06blwm). The creation of this dataset is in response to feedback from Professor Huang's proposal, aiming for a deeper exploration and analysis of Shitao's artworks. The collection and processing of the dataset involve web scraping scripts, data processing, and tagging with the bilp2 model, with these scripts and related codes made public in a GitHub repository (link: https://github.com/mhy-666/artwork_for_sdxl_dataset).

The schema of the dataset comprises two features: prompt and image_data, representing the description of the artworks (string type) and image data (image type), respectively. The dataset is divided into a train split, containing 41 examples, with a total data size of 1,339,678 bytes and a download size of 4,170 bytes.

The existence of this dataset serves to provide a basis for in-depth analysis and model training specifically for Shitao's artworks, promoting the digital study and popularization of classical Chinese art. By focusing on the works of a single artist, the dataset aims to explore the expression and characteristics of a specific art style, supporting automatic annotation and classification research of artworks.

However, the dataset has certain limitations. First, the relatively small number of samples (only 41 examples) may limit the breadth and generalization ability of model training. Second, focusing on the works of a single artist, while beneficial for in-depth studies of a particular style, may result in the model's poorer adaptability to other art styles. Additionally, as the dataset's construction involves web scraping, it may be affected by website updates or changes in copyright policies, leading to uncertainties in data access and use.

Here is the use case for this dataset: https://colab.research.google.com/drive/1Gmpu1pO3D-H8rWnTe68V49W15LVobYM6?usp=sharing

This is the Stable Diffusion paper: arxiv: arxiv.org/abs/2112.10752

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