--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kanade/カナデ (Beast Tamer) This is the dataset of Kanade/カナデ (Beast Tamer), containing 524 images and their tags. The core tags of this character are `long_hair, animal_ears, animal_ear_fluff, ribbon, hair_ribbon, green_eyes, cat_ears, orange_hair, red_ribbon, brown_hair`, 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-others | Images-head | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|:----------------|:--------------| | raw | 524 | 420.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanade_beasttamer/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | -- | -- | | stage3-p480-1200 | 1086 | 766.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanade_beasttamer/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | 487 | 599 | ### 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/kanade_beasttamer', 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 | 18 |  |  |  |  |  | 1girl, bare_shoulders, solo, upper_body, collarbone, closed_mouth, outdoors, v-shaped_eyebrows, off-shoulder_shirt | | 1 | 12 |  |  |  |  |  | 1girl, bare_shoulders, day, off-shoulder_shirt, outdoors, upper_body, blue_sky, cloud, collarbone, solo, closed_mouth, anime_coloring, looking_at_viewer, yellow_shirt | | 2 | 7 |  |  |  |  |  | 1girl, bare_shoulders, collarbone, open_mouth, solo, fang, upper_body, off-shoulder_shirt, anime_coloring, blush | | 3 | 6 |  |  |  |  |  | 1girl, bare_shoulders, collarbone, forest, off-shoulder_shirt, outdoors, solo, upper_body, anime_coloring, open_mouth, tree, closed_eyes, breasts | | 4 | 5 |  |  |  |  |  | 1girl, bare_shoulders, closed_mouth, looking_at_viewer, portrait, solo, collarbone, v-shaped_eyebrows | | 5 | 5 |  |  |  |  |  | 1girl, anime_coloring, bare_shoulders, breasts, collarbone, outdoors, solo, :d, blurry_background, cat_girl, cat_tail, day, dress, fang, open_mouth, short_sleeves, tree, looking_at_viewer, off-shoulder_shirt, yellow_shirt, belt, bow, green_shirt, very_long_hair | | 6 | 5 |  |  |  |  |  | 1girl, blue_sky, day, fang, fox_ears, open_mouth, solo, anime_coloring, bare_shoulders, cloud, outdoors, fox_girl, blush, shirt, style_parody, v-shaped_eyebrows | | 7 | 5 |  |  |  |  |  | 1girl, chibi, full_body, green_shirt, pleated_skirt, socks, standing, white_skirt, ^_^, cat_girl, cat_tail, collarbone, facing_viewer, solo, :3, :d, open_mouth, outstretched_arms, puffy_short_sleeves, bare_shoulders, bow, brown_footwear, fang, hair_between_eyes, off-shoulder_shirt, very_long_hair | | 8 | 5 |  |  |  |  |  | 1girl, light_censor, light_rays, solo, blush, fang, open_mouth, wavy_mouth, breasts, completely_nude, sunlight, upper_body, water, very_long_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | solo | upper_body | collarbone | closed_mouth | outdoors | v-shaped_eyebrows | off-shoulder_shirt | day | blue_sky | cloud | anime_coloring | looking_at_viewer | yellow_shirt | open_mouth | fang | blush | forest | tree | closed_eyes | breasts | portrait | :d | blurry_background | cat_girl | cat_tail | dress | short_sleeves | belt | bow | green_shirt | very_long_hair | fox_ears | fox_girl | shirt | style_parody | chibi | full_body | pleated_skirt | socks | standing | white_skirt | ^_^ | facing_viewer | :3 | outstretched_arms | puffy_short_sleeves | brown_footwear | hair_between_eyes | light_censor | light_rays | wavy_mouth | completely_nude | sunlight | water | |----:|----------:|:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:--------|:-----------------|:-------|:-------------|:-------------|:---------------|:-----------|:--------------------|:---------------------|:------|:-----------|:--------|:-----------------|:--------------------|:---------------|:-------------|:-------|:--------|:---------|:-------|:--------------|:----------|:-----------|:-----|:--------------------|:-----------|:-----------|:--------|:----------------|:-------|:------|:--------------|:-----------------|:-----------|:-----------|:--------|:---------------|:--------|:------------|:----------------|:--------|:-----------|:--------------|:------|:----------------|:-----|:--------------------|:----------------------|:-----------------|:--------------------|:---------------|:-------------|:-------------|:------------------|:-----------|:--------| | 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | X | X | X | X | | | | X | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 |  |  |  |  |  | X | X | X | X | X | | X | | X | | | | X | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | X | X | X | | X | X | | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 |  |  |  |  |  | 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 | 5 |  |  |  |  |  | X | X | X | | | | X | X | | X | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 7 | 5 |  |  |  |  |  | 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 | X | | | | | | | | 8 | 5 |  |  |  |  |  | X | | X | X | | | | | | | | | | | | X | X | X | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |
