metadata

size_categories:
  - 1K<n<10K
license: other
tags:
  - ghibli
  - ai-generated
  - image-classification
language:
  - en
pretty_name: Ghibli Real vs AI Dataset
task_categories:
  - image-classification
task_ids:
  - multi-class-classification
splits:
  - name: train
    num_examples: 4257
annotations_creators:
  - machine-generated
source_datasets:
  - Nechintosh/ghibli
  - nitrosocke/Ghibli-Diffusion
  - KappaNeuro/studio-ghibli-style
dataset_info:
  labels:
    - real
    - ai

Ghibli Real vs AI-Generated Dataset

One sample per line
Includes: id, image, label, description
Use this for standard classification or image-text training
Real images sourced from Nechintosh/ghibli (810 images)
AI-generated images created using:
- nitrosocke/Ghibli-Diffusion (2637 images)
- KappaNeuro/studio-ghibli-style (810 images)
- Note: While the KappaNeuro repository does not explicitly state a license, it is a fine-tuned model based on Stable Diffusion XL, which is distributed under the CreativeML Open RAIL++-M License. Therefore, it is assumed that this model inherits the same license and non-commercial restrictions.

How to load

from datasets import load_dataset

samples = load_dataset("pulnip/ghibli-dataset", split="train")

# Convert labels to binary classification: 'real' vs 'ai'
# Note: The original "label" field contains "real", "nitrosocke", and "KappaNeuro".
#       You can treat all non-"real" labels as "ai" to use this dataset for binary classification.
for sample in samples:
    sample["binary_label"] = "real" if sample["label"] == "real" else "ai"

License and Usage

This dataset combines data from multiple sources. Please review the licensing conditions carefully.

Real Images

Source: Nechintosh/ghibli
License: Not explicitly stated; assumed for non-commercial research use only

AI-Generated Images

Source models:
- nitrosocke/Ghibli-Diffusion
  Loaded with: torch_dtype=torch.float16
- KappaNeuro/studio-ghibli-style
  Loaded with: torch_dtype=torch.float16, variant="fp16"
These models are provided under community licenses that generally restrict usage to non-commercial and research purposes.

Summary

This repository is not published under a single license such as MIT.
Because the dataset includes content from multiple sources with varying restrictions,
the dataset is licensed under 'other' and should be treated as non-commercial research-use only.

Users are responsible for reviewing each component’s license terms before redistribution or adaptation.