metadata
dataset_info:
features:
- name: approver_id
dtype: float64
- name: bit_flags
dtype: int64
- name: created_at
dtype: string
- name: down_score
dtype: int64
- name: fav_count
dtype: int64
- name: file_ext
dtype: string
- name: file_size
dtype: int64
- name: file_url
dtype: string
- name: has_active_children
dtype: bool
- name: has_children
dtype: bool
- name: has_large
dtype: bool
- name: has_visible_children
dtype: bool
- name: id
dtype: int64
- name: image_height
dtype: int64
- name: image_width
dtype: int64
- name: is_banned
dtype: bool
- name: is_deleted
dtype: bool
- name: is_flagged
dtype: bool
- name: is_pending
dtype: bool
- name: large_file_url
dtype: string
- name: last_comment_bumped_at
dtype: string
- name: last_commented_at
dtype: string
- name: last_noted_at
dtype: string
- name: md5
dtype: string
- name: media_asset_created_at
dtype: string
- name: media_asset_duration
dtype: float64
- name: media_asset_file_ext
dtype: string
- name: media_asset_file_key
dtype: string
- name: media_asset_file_size
dtype: int64
- name: media_asset_id
dtype: int64
- name: media_asset_image_height
dtype: int64
- name: media_asset_image_width
dtype: int64
- name: media_asset_is_public
dtype: bool
- name: media_asset_md5
dtype: string
- name: media_asset_pixel_hash
dtype: string
- name: media_asset_status
dtype: string
- name: media_asset_updated_at
dtype: string
- name: media_asset_variants
dtype: string
- name: parent_id
dtype: float64
- name: pixiv_id
dtype: float64
- name: preview_file_url
dtype: string
- name: rating
dtype: string
- name: score
dtype: int64
- name: source
dtype: string
- name: tag_count
dtype: int64
- name: tag_count_artist
dtype: int64
- name: tag_count_character
dtype: int64
- name: tag_count_copyright
dtype: int64
- name: tag_count_general
dtype: int64
- name: tag_count_meta
dtype: int64
- name: tag_string
dtype: string
- name: tag_string_artist
dtype: string
- name: tag_string_character
dtype: string
- name: tag_string_copyright
dtype: string
- name: tag_string_general
dtype: string
- name: tag_string_meta
dtype: string
- name: up_score
dtype: int64
- name: updated_at
dtype: string
- name: uploader_id
dtype: int64
splits:
- name: train
num_bytes: 20051410186
num_examples: 8616173
download_size: 7310216883
dataset_size: 20051410186
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- text-to-image
- image-classification
language:
- en
- ja
pretty_name: Danbooru 2025 Metadata
size_categories:
- 1M<n<10M
Dataset Card for Danbooru 2025 Metadata
This dataset repo provides comprehensive, up-to-date metadata for the Danbooru booru site. All metadata was freshly scraped starting on January 2, 2025, resulting in more extensive tag annotations for older posts, fewer errors, and reduced occurrences of non-labelled AI-generated images in the data.
Dataset Details
What is this?
A refreshed, Parquet-formatted metadata dump of Danbooru, current as of January 2, 2025.
Why this over other Danbooru scrapes?
- Fresh Metadata: Coverage includes post IDs from 1 through ~8.6M, with the newest vocabulary and tag annotations.
- Maximized Tag Count: Many historical tag renames and additions are accurately reflected, reducing duplications for downstream tasks.
- Reduced Noise: Fewer untagged or mislabeled AI images compared to older scrapes.
Tag Comparisons
[TODO: Contrast the tag counts, deleted entries, etc. with other Danbooru metadata scrapes.]
- Shared by: trojblue
- Language(s) (NLP): English, Japanese
- License: MIT
Uses
The dataset can be loaded or filtered with the Huggingface datasets library:
from datasets import Dataset, load_dataset
danbooru_dataset = load_dataset("trojblue/danbooru2025-metadata", split="train")
df = danbooru_dataset.to_pandas()
This dataset can be used to:
- Retrieve the full Danbooru image set via the metadata’s URLs
- Train or fine-tune an image tagger
- Compare against previous metadata versions to track changes, tag evolution, and historical trends
Dataset Structure
Below is a partial overview of the DataFrame columns, derived directly from the Danbooru JSONs:
import unibox as ub
ub.peeks(df)
(8616173, 59)
Index(['approver_id', 'bit_flags', 'created_at', 'down_score', 'fav_count',
'file_ext', 'file_size', 'file_url', 'has_active_children',
'has_children', 'has_large', 'has_visible_children', 'id',
'image_height', 'image_width', 'is_banned', 'is_deleted', 'is_flagged',
'is_pending', 'large_file_url', 'last_comment_bumped_at',
'last_commented_at', 'last_noted_at', 'md5', 'media_asset_created_at',
'media_asset_duration', 'media_asset_file_ext', 'media_asset_file_key',
'media_asset_file_size', 'media_asset_id', 'media_asset_image_height',
'media_asset_image_width', 'media_asset_is_public', 'media_asset_md5',
'media_asset_pixel_hash', 'media_asset_status',
'media_asset_updated_at', 'media_asset_variants', 'parent_id',
'pixiv_id', 'preview_file_url', 'rating', 'score', 'source',
'tag_count', 'tag_count_artist', 'tag_count_character',
'tag_count_copyright', 'tag_count_general', 'tag_count_meta',
'tag_string', 'tag_string_artist', 'tag_string_character',
'tag_string_copyright', 'tag_string_general', 'tag_string_meta',
'up_score', 'updated_at', 'uploader_id'],
dtype='object')
| approver_id | bit_flags | created_at | down_score | fav_count | file_ext | file_size | file_url | has_active_children | has_children | ... | tag_count_meta | tag_string | tag_string_artist | tag_string_character | tag_string_copyright | tag_string_general | tag_string_meta | up_score | updated_at | uploader_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | 0 | 2015-08-07T23:23:45.072-04:00 | 0 | 66 | jpg | 4134797 | https://cdn.donmai.us/original/a1/b3/a1b3d0fa9... | False | False | ... | 3 | 1girl absurdres ass bangle bikini black_bikini... | kyouka. | marie_(splatoon) | splatoon_(series) splatoon_1 | 1girl ass bangle bikini black_bikini blush bra... | absurdres commentary_request highres | 15 | 2024-06-25T15:32:44.291-04:00 | 420773 |
| 1 | NaN | 0 | 2008-03-05T01:52:28.194-05:00 | 0 | 7 | jpg | 380323 | https://cdn.donmai.us/original/d6/10/d6107a13b... | False | False | ... | 2 | 1girl aqua_hair bad_id bad_pixiv_id guitar hat... | shimeko | hatsune_miku | vocaloid | 1girl aqua_hair guitar instrument long_hair so... | bad_id bad_pixiv_id | 4 | 2018-01-23T00:32:10.080-05:00 | 1309 |
| 2 | 85307.0 | 0 | 2015-08-07T23:26:12.355-04:00 | 0 | 10 | jpg | 208409 | https://cdn.donmai.us/original/a1/2c/a12ce629f... | False | False | ... | 1 | 1boy 1girl blush boots carrying closed_eyes co... | yuuryuu_nagare | jon_(pixiv_fantasia_iii) race_(pixiv_fantasia) | pixiv_fantasia pixiv_fantasia_3 | 1boy 1girl blush boots carrying closed_eyes da... | commentary_request | 3 | 2022-05-25T02:26:06.588-04:00 | 95963 |
Dataset Creation
We scraped all post IDs on Danbooru from 1 up to the latest. Some restricted tags (e.g. loli) were hidden by the site and require a gold account to access, so they are not present.
For a more complete (but older) metadata reference, you may wish to combine this with Danbooru2021 or similar previous scrapes.
The scraping process used a pool of roughly 400 IPs over six hours, ensuring consistent tag definitions. Below is a simplified example of the process used to convert the metadata into Parquet:
import pandas as pd
from pandarallel import pandarallel
# Initialize pandarallel
pandarallel.initialize(nb_workers=4, progress_bar=True)
def flatten_dict(d, parent_key='', sep='_'):
"""
Flattens a nested dictionary.
"""
items = []
for k, v in d.items():
new_key = f"{parent_key}{sep}{k}" if parent_key else k
if isinstance(v, dict):
items.extend(flatten_dict(v, new_key, sep=sep).items())
elif isinstance(v, list):
items.append((new_key, ', '.join(map(str, v))))
else:
items.append((new_key, v))
return dict(items)
def extract_all_illust_info(json_content):
"""
Parses and flattens Danbooru JSON into a pandas Series.
"""
flattened_data = flatten_dict(json_content)
return pd.Series(flattened_data)
def dicts_to_dataframe_parallel(dicts):
"""
Converts a list of dicts to a flattened DataFrame using pandarallel.
"""
df = pd.DataFrame(dicts)
flattened_df = df.parallel_apply(lambda row: extract_all_illust_info(row.to_dict()), axis=1)
return flattened_df
Recommendations
Users should be aware of potential biases and limitations, including the presence of adult content in some tags. More details and mitigations may be needed.