zerochan / README.md
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---
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<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.