Datasets:
metadata
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: tag_probs
sequence: float32
- name: class
dtype:
class_label:
names:
'0': not_bookmarked
'1': bookmarked_public
'2': bookmarked_private
splits:
- name: train
num_bytes: 4301053452
num_examples: 179121
- name: test
num_bytes: 1433684484
num_examples: 59707
- name: validation
num_bytes: 1433708496
num_examples: 59708
download_size: 7351682183
dataset_size: 7168446432
task_categories:
- image-classification
- tabular-classification
tags:
- art
size_categories:
- 100K<n<1M
The dataset for training classification model of pixiv artworks by my preference.
Schema
- tag_probs: List of probabilities for each tag. Preprocessed by RF5/danbooru-pretrained model. The index of each probability corresponds to the index of the tag in the class_names_6000.json file.
- class:
- not_bookmarked (0): Generated from images randomly-sampled from animelover/danbooru2022 dataset. The images are filtered in advance to the post with pixiv source.
- bookmarked_public (1): Generated from publicly bookmarked images of hakatashi.
- bookmarked_private (2): Generated from privately bookmarked images of hakatashi.
Stats
train:test:validation = 6:2:2
- not_bookmarked (0): 202,290 images
- bookmarked_public (1): 73,587 images
- bookmarked_private (2): 22,659 images
Usage
>>> from datasets import load_dataset
>>> dataset = load_dataset("hakatashi/hakatashi-pixiv-bookmark-deepdanbooru")
>>> dataset
DatasetDict({
test: Dataset({
features: ['tag_probs', 'class'],
num_rows: 59707
})
train: Dataset({
features: ['tag_probs', 'class'],
num_rows: 179121
})
validation: Dataset({
features: ['tag_probs', 'class'],
num_rows: 59708
})
})
>>> dataset['train'].features
{'tag_probs': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None),
'class': ClassLabel(names=['not_bookmarked', 'bookmarked_public', 'bookmarked_private'], id=None)}