Datasets:

joelseytre
/

toonout

Tasks:

Image Segmentation

Size:

1K<n<10K

ArXiv:

License:

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Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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ToonOut Dataset

Please check out:

our repository: https://github.com/MatteoKartoon/BiRefNet
our paper: ToonOut: Fine-tuned Background Removal for Anime Characters
the weights for BiRefNet, fine-tuned on this dataset

Dataset Summary

The ToonOut Dataset is a collection of 1,228 high-quality anime-style images annotated for background removal tasks. Each sample includes raw RGB images, ground truth transparency masks, and RGBA annotated images with alpha channels. The dataset was specifically curated to improve segmentation and matting performance on anime characters and objects, addressing challenges such as complex hair, transparency, and stylized features.

It was used to fine-tune the BiRefNet model, resulting in ToonOut, which significantly improves background removal accuracy for anime-style images.

License: CC-BY 4.0 (dataset)
Intended task: Image segmentation (foreground/background separation for anime characters)
Size: 1,228 annotated images (with train/val/test splits)

Dataset Details

Uses

Direct Use

Training, fine-tuning, or benchmarking background removal models for anime-style content. .

Sample Usage

To get started with the associated code and dataset, follow the installation steps and then use the provided scripts for training and evaluation.

Installation

git clone https://github.com/MatteoKartoon/BiRefNet.git
cd BiRefNet
pip install -r requirements.txt

Inference demo

Please see the provided notebook here.

Training

bash bash_scripts/train_finetuning.sh

Evaluation

python scripts/evaluations.py --checkpoint path/to/checkpoint

Dataset Structure

The dataset is split into train, validation, and test sets. Each split is organized into generation folders containing three subfolders:

im/: Raw RGB images
gt/: Ground truth transparency masks
an/: RGBA annotated images (RGB + alpha)

toonout_dataset/
├── train/
│   ├── train_generations_20250318_emotion/
│   │   ├── im/    # RGB images
│   │   ├── gt/    # Ground truth masks
│   │   └── an/    # RGBA annotated images
│   └── train_generations_/…
├── test/
│   └── test_generations_/…
└── val/
└── validation_generations_*/…

Dataset Creation

Curation Rationale

State-of-the-art background removal models underperform on stylized anime imagery, particularly with fine-grained details such as hair strands, line art, and semi-transparent effects. This dataset was curated to bridge that gap and provide training material for anime-specific background-removal models.

Source Data

Data Collection and Processing

Images were curated from synthetic generations of anime-style images (Stable Difussion XL, yamer's anime checkpoint).
Expert annotators manually produced transparency masks for each image, starting from a model-generated annotation.
Splits (train/val/test) were created with care to avoid overlap.

Who are the source data producers?

The dataset was created and annotated by researchers at Kartoon AI and collaborators.

Annotations

Annotation Process

Annotation type: Foreground/background transparency masks.
Tools used: Manual tools for annotation of transparency masks.
Quality control: Manual inspection and consistency checks across the dataset.

Who are the annotators?

Researchers at Kartoon AI curated and validated the annotations.

Personal and Sensitive Information

The dataset contains no personal, private, or sensitive information. It consists exclusively of synthetic images.

Citation

If you use this dataset, please cite the accompanying paper:

BibTeX:

@misc{muratori2025toonout,
  title={ToonOut: Fine-tuned Background Removal for Anime Characters},
  author={Muratori, Matteo and Seytre, Joël},
  year={2025},
  eprint={2509.06839},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2509.06839},
  doi={10.48550/arXiv.2509.06839}
}

⸻

Dataset Card Authors: Joël Seytre (Kartoon AI)

Dataset Card Contact: Joël Seytre ([email protected]), Matteo Muratori ([email protected])

⸻

Project by Kartoon AI, powering toongether, check us out at kartoon.ai & toongether.ai