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---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of 37/重返未来37/37(リバース:1999) (Reverse:1999)
This is the dataset of 37/重返未来37/37(リバース:1999) (Reverse:1999), containing 168 images and their tags.
The core tags of this character are `long_hair, blue_hair, blue_eyes, very_long_hair, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description | Images-head | Images-others |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|:--------------|:----------------|
| raw | 168 | 386.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/37_reverse1999/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- |
| stage3-p480-1200 | 416 | 584.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/37_reverse1999/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | 159 | 257 |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/37_reverse1999',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, ancient_greek_clothes, solo, greco-roman_clothes, toga, barefoot, full_body, white_dress, sitting, closed_mouth, anklet, bare_shoulders, gold_choker, looking_at_viewer, sky, feet, holding, smile, toes |
| 1 | 8 |  |  |  |  |  | 1girl, ancient_greek_clothes, greco-roman_clothes, solo, toga, white_dress, gold_choker, parted_lips, looking_at_viewer, bare_shoulders, floating_hair, armlet, jewelry, star_(sky) |
| 2 | 6 |  |  |  |  |  | 1girl, ancient_greek_clothes, collarbone, gold_choker, greco-roman_clothes, solo, toga, arm_up, bare_shoulders, braid, off_shoulder, holding, jewelry, long_sleeves, parted_lips, single_bare_shoulder, breasts, cowboy_shot, looking_at_viewer, outdoors, sky, white_dress |
| 3 | 9 |  |  |  |  |  | 1girl, gold_choker, simple_background, solo, white_background, collarbone, looking_at_viewer, ancient_greek_clothes, greco-roman_clothes, toga, upper_body, bare_shoulders, closed_mouth, dress, hairband, hand_up |
| 4 | 5 |  |  |  |  |  | ancient_greek_clothes, bare_shoulders, gold_choker, greco-roman_clothes, simple_background, smile, toga, white_background, collarbone, upper_body, 1girl, closed_mouth, looking_at_viewer, red_hair, solo_focus, 1boy, armlet, hair_over_one_eye, jewelry, white_dress |
| 5 | 8 |  |  |  |  |  | 1girl, solo, white_dress, bare_shoulders, bird, ocean, outdoors, toga, water, ancient_greek_clothes, gold_choker, greco-roman_clothes, armlet, blue_sky, cloud, day, floating_hair, from_side, looking_at_viewer, braid, closed_mouth, hand_up, jewelry, open_mouth, profile |
| 6 | 10 |  |  |  |  |  | 1girl, solo, green_dress, veil, braid, looking_at_viewer, necklace, white_flower, closed_mouth, hair_flower, ribbon, sleeveless, smile, beads, aqua_eyes, bird, holding |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | ancient_greek_clothes | solo | greco-roman_clothes | toga | barefoot | full_body | white_dress | sitting | closed_mouth | anklet | bare_shoulders | gold_choker | looking_at_viewer | sky | feet | holding | smile | toes | parted_lips | floating_hair | armlet | jewelry | star_(sky) | collarbone | arm_up | braid | off_shoulder | long_sleeves | single_bare_shoulder | breasts | cowboy_shot | outdoors | simple_background | white_background | upper_body | dress | hairband | hand_up | red_hair | solo_focus | 1boy | hair_over_one_eye | bird | ocean | water | blue_sky | cloud | day | from_side | open_mouth | profile | green_dress | veil | necklace | white_flower | hair_flower | ribbon | sleeveless | beads | aqua_eyes |
|----:|----------:|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:--------|:------------------------|:-------|:----------------------|:-------|:-----------|:------------|:--------------|:----------|:---------------|:---------|:-----------------|:--------------|:--------------------|:------|:-------|:----------|:--------|:-------|:--------------|:----------------|:---------|:----------|:-------------|:-------------|:---------|:--------|:---------------|:---------------|:-----------------------|:----------|:--------------|:-----------|:--------------------|:-------------------|:-------------|:--------|:-----------|:----------|:-----------|:-------------|:-------|:--------------------|:-------|:--------|:--------|:-----------|:--------|:------|:------------|:-------------|:----------|:--------------|:-------|:-----------|:---------------|:--------------|:---------|:-------------|:--------|:------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | X | | | X | | | | X | X | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | X | | | X | | | | X | X | X | X | | X | | | X | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | X | X | X | | | | | X | | X | X | X | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | | X | X | | | X | | X | | X | X | X | | | | X | | | | X | X | | X | | | | | | | | | X | X | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | X | X | X | X | | | X | | X | | X | X | X | | | | | | | X | X | X | | | | X | | | | | | X | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 6 | 10 |  |  |  |  |  | X | | X | | | | | | | X | | | | X | | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X |
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