README.md

---
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    path: 20250413/train-*
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    path: 20250605/train-*
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task_categories:
- text-to-image
language:
- en
tags:
- anime
- image
- mirror
pretty_name: Zerochan
size_categories:
- 1M<n<10M
---

# CommonArch - Zerochan

## Description

This dataset contains a dump of image resources from the website Zerochan, encompassing a wide variety of anime-style illustrations. 
It aims to provide a comprehensive collection for training anime-style image generation models and other related research.

This dataset contains **3,843,124** illustrations scraped up to 2024-10-19 11:57:31 UTC.and is regularly updated every 3 months via web crawling. 
The next update is scheduled for the end of March 2025.

## Dataset Details

* **Number of Images:** 3,843,124
* **Image Quality:** Generally of "medium" quality.
* **Image Format:** Includes various formats such as JPG, PNG, and WEBP.
* **Storage Format:** Parquet

This dataset is stored in Parquet format to facilitate integration with the Hugging Face ecosystem. 
The original image formats (JPG, PNG, WEBP) are preserved during storage.

**Data Sampling:** The files have been re-ordered during conversion to ensure each file is a reasonably random sample of the parent dataset. 
You can easily obtain a random subset by loading only a portion of the files (e.g., `"hf://datasets/zenless-lab/zerochan/data/train-0000[12]-*.parquet"`).

## Image Quality Distribution

(*** WIP ***)

## Data Origin and Processing

This dataset is converted from the DeepGHS's [deepghs/zerochan_full](https://huggingface.co/datasets/deepghs/zerochan_full). 
The content is updated every 3 months through web crawling.

## Columns

The dataset contains the following columns:

*   **id**: Unique identifier (unsorted).
*   **filename**: Original filename from `deepghs/zerochan_full`, usually in the format `<id>.<ext>`.
*   **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.