--- dataset_info: - config_name: default features: - name: id dtype: int64 - name: filename dtype: string - name: mimetype dtype: string - name: width dtype: int64 - name: height dtype: int64 - name: file_url dtype: string - name: file_size dtype: int64 - name: small_url dtype: string - name: medium_url dtype: string - name: large_url dtype: string - name: hash dtype: string - name: source dtype: string - name: primary_tag dtype: string - name: tags sequence: string - name: tag_info struct: - name: character sequence: string - name: game sequence: string - name: group sequence: string - name: mangaka sequence: string - name: meta sequence: string - name: movie sequence: string - name: outfit sequence: string - name: series sequence: string - name: source sequence: string - name: source-copyright sequence: string - name: studio sequence: string - name: theme sequence: string - name: unknown sequence: string - name: vtuber sequence: string - name: image dtype: image splits: - name: train num_bytes: 5671866874980.54 num_examples: 3843124 download_size: 5691651895890 dataset_size: 5671866874980.54 - config_name: metadata features: - name: id dtype: int64 - name: filename dtype: string - name: mimetype dtype: string - name: width dtype: int64 - name: height dtype: int64 - name: file_url dtype: string - name: file_size dtype: int64 - name: small_url dtype: string - name: medium_url dtype: string - name: large_url dtype: string - name: hash dtype: string - name: source dtype: string - name: primary_tag dtype: string - name: tags sequence: string - name: tag_info struct: - name: character sequence: string - name: game sequence: string - name: group sequence: string - name: mangaka sequence: string - name: meta sequence: string - name: movie sequence: string - name: outfit sequence: string - name: series sequence: string - name: source sequence: string - name: source-copyright sequence: string - name: studio sequence: string - name: theme sequence: string - name: unknown sequence: string - name: vtuber sequence: string - name: mod dtype: int64 splits: - name: train num_bytes: 3537321017 num_examples: 3843124 download_size: 963680600 dataset_size: 3537321017 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: metadata data_files: - split: train path: metadata/train-* task_categories: - text-to-image language: - en tags: - anime - image - mirror pretty_name: Zerochan size_categories: - 1M.`. * **minetype**: The MIME type of the downloaded image (e.g., `image/jpeg`, `image/png`). * **width**: Original image width. * **height**: Original image height. * **file_url**: Original image URL. * **file_size**: Image file size (in bytes). * **small_url**: URL of the small preview image. * **medium_url**: URL of the medium preview image. * **large_url**: URL of the large preview image. * **hash**: Hash of the image. * **source**: Source of the image (only available for some rows). * **primary_tag**: Primary tag associated with the image. * **tags**: Original tags associated with the image. * **tag_info**: Structured information about the tags, categorized by character, copyright, etc. * **image**: The dumped original image file (binary data). ## Intended Use This dataset is primarily intended for training anime-style image generation models. It can also be used for other research purposes related to image analysis and style transfer. ## Usage The `image` column contains a dictionary with keys `bytes` and `path`. When using the `datasets` library, the images are automatically converted to PIL Images. However, when using other libraries, you may need to pre-process the images to convert them to PIL format. Here are some examples of how to load and use the dataset with different libraries: **Using `datasets`:** ```python from datasets import load_dataset ds = load_dataset("parquet", data_files="hf://datasets/zenless-lab/zerochan/data/train-0000[12]-*.parquet", split="train") print(ds) # Access the first image (automatically converted to PIL Image) image = ds[0]['image'] image.show() # Access the tags for the first image tags = ds[0]['tags'] print(tags) ``` **Using `Dask`:** ```python import dask.dataframe as dd from PIL import Image import io def convert_image(row): if not isinstance(row, dict): return None img_bytes = io.BytesIO(row["bytes"]) img = Image.open(img_bytes) img = img.convert("RGB") return img df = dd.read_parquet("hf://datasets/zenless-lab/zerochan/data/train-0000[12]-*.parquet") df["image"] = df["image"].map(convert_image) print(df.head(10)) # to avoid pulling all data during debugging. ``` **Using `Polars`:** ```python import polars as pl from PIL import Image import io df = pl.read_parquet('hf://datasets/zenless-lab/zerochan/data/train-0000[12]-*.parquet') df = df.with_columns( pl.col("image").map_elements(lambda x: Image.open(io.BytesIO(x["bytes"])), return_dtype=pl.Object) ) print(df) ``` ## License **Important Notice Regarding Licensing:** This dataset does not currently have a defined license. **Before using this dataset, it is your responsibility to ensure that your use complies with the copyright laws in your jurisdiction.** You should determine whether your use qualifies for an exception or limitation to copyright, such as fair use (in the US) or similar provisions like Article 30-4 of the Japanese Copyright Act or the EU Directive on Copyright in the Digital Single Market. **This is not legal advice; please consult with a legal professional to assess the risks associated with your intended use.** ## Contributing Contributions to this dataset are welcome! You can contribute by: * Reporting issues or suggesting improvements. * Submitting new images (please ensure they comply with the website's terms of service). * Correcting or improving tags and metadata. ## Contact If you have any questions or issues, please feel free to open an issue on the [Hugging Face Dataset repository](https://huggingface.co/datasets/zenless-lab/zerochan). ## Future Updates The dataset will be updated approximately every three months with new images.