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README.md

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-# Danbooru 2023
+# [Mirror]Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset
+
+Danbooru2023 is an extension of Danbooru2021, featuring over 6.8 million anime-style images, totaling more than 8.3 TB. 
+Each image is accompanied by community-contributed tags that provide detailed descriptions of its content, including characters, 
+artists, copyright information, concepts, and attire. 
+This makes it a crucial resource for stylized computer vision tasks and transfer learning.
+
+> This dataset mirrors [nyanko7/danbooru2023](https://huggingface.co/datasets/nyanko7/danbooru2023).
+> It has been converted to Parquet format to optimize compatibility with the Hugging Face ecosystem 
+> and enhance streaming performance. We gratefully acknowledge the excellent work contributed by Nyanko Devs (nyanko7).
+
+To ensure research reproducibility, **this dataset will not receive future updates, 
+even if the original dataset (which has not been updated for over a year) undergoes changes**. 
+As the license is inherited from the original dataset, any copyright disputes affecting it will lead to corresponding adjustments in this mirror. 
+For DOI requests, please open an issue on the repository.
+
+The dataset includes:
+
+- Image Files: Numerous anime-style images, varying in resolution, stored as raw bytes within Parquet files.
+- Tag Data: Textual tags associated with each image, manually added and reviewed by community users.
+- Other Metadata: Includes image upload time, ratings, favorite counts, and original URLs.
+
+## Dataset Details
+
+- **Curated by:** Nyanko Devs (nyanko7)
+- **Language(s):** Primarily English, also containing some Japanese or other Romaji spellings.
+- **License**: MIT
+- **Repository:** https://huggingface.co/datasets/zenless-lab/danbooru2023
+- **Original Repository:** https://huggingface.co/datasets/nyanko7/danbooru2023
+
+## Intended Use
+
+This dataset was initially employed for research into catastrophic forgetting during transfer learning, 
+especially when substantial distribution disparities exist between pre-training and fine-tuning datasets. 
+
+It is also well-suited for training anime-style image generation models and other research objectives, including image analysis.
+
+## Usage
+
+Given the substantial size of this dataset, it can be accessed using the `datasets` library's streaming functionality (in 1GB blocks) 
+or by leveraging the glob method to acquire a local, random subset.
+
+``` python
+from datasets import load_dataset
+
+ds = load_dataset("zenless-lab/danbooru2023", split='train', streaming=True)
+next(iter(ds))
+```
+
+Alternatively, directly use glob to acquire a local, random subset:
+``` python
+from datasets import load_dataset
+
+# The method below will read the first and second Parquet blocks, downloading approximately 2GB of data.
+ds = load_dataset("parquet", data_files="hf://datasets/zenless-lab/danbooru2023/data/train-0000[12]-*.parquet", split="train")
+next(iter(ds))
+```
+
+## Bias, Risks, and Limitations
+
+The image quality within the Danbooru2023 dataset is inconsistent, and it contains a significant proportion of R18 and R18G content. 
+As not all image origins can be traced, proper authorization cannot be guaranteed; therefore, users are strongly advised to exercise discretion prior to use. 
+The dataset may exhibit a strong bias towards styles preferred by the English-speaking community, 
+a reflection of the original uploaders' preferences. 
+
+While images have unique UIDs, duplicates may still be present as **fuzzy deduplication was not performed**.
+
+## Licensing
+
+This dataset operates under the MIT license, inherited from `nyanko7/danbooru2023`. 
+However, **no independent copyright investigation of the included images has been conducted**. 
+Users are strongly advised to ensure their intended use adheres to the copyright laws of their respective jurisdictions, 
+determining if it qualifies for applicable copyright exemptions 
+(e.g., *fair use* in the U.S., *Article 30-4 of the Japanese Copyright Act* in Japan, or the *Article 3/4(TDM exemption) of DSM* in EU). 
+**This document does not constitute legal advice**; consultation with a qualified legal professional is recommended to evaluate potential risks.