All datasets / deepghs

deepghs/anime_blurry_preparation

31 downloads in 30 days3 likesLast modified 17 Nov 2025View on Hugging Face

Permissive or share-alike

The declared licence (cc-by-4.0) is classified as permissive.

Declared licence: cc-by-4.0, from the tagEvidence: README.md at 0f59722
Licence files: No licence file at the top level of the repositoryEvidence: file list at 0f59722
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

Each cell links to what was checked: a file, a file list or a Hub record.

Tasks

image-classification

Citation text from the card

@misc{anime_blurry_preparation,
  title        = {Anime Blurry Preparation Dataset},
  author       = {deepghs},
  howpublished = {\url{https://huggingface.co/datasets/deepghs/anime_blurry_preparation}},
  year         = {2023},
  note         = {Binary classification dataset for clear vs blurry 3D anime images with automatic BLIP annotation},
  abstract     = {The Anime Blurry Preparation Dataset is a specialized binary classification dataset designed for distinguishing between clear and blurry 3D anime-style images. This dataset provides a comprehensive collection of image pairs where each clear image has a corresponding blurry counterpart, making it ideal for training and evaluating image quality assessment models. The dataset employs automatic annotation techniques using BLIP to generate reliable labels for the classification task. This dataset specifically focuses on 3D anime-style content, addressing the unique challenges of image quality assessment in computer-generated anime imagery. The clear images represent high-quality, sharp renderings while the blurry counterparts simulate various types of image degradation that commonly occur during rendering, compression, or transmission processes.},
  keywords     = {binary classification, image-classification, computer-vision, anime, 3d}
}

All datasets marked Permissive or share-alike | JSON

The same facts for your agent

A free MCP server at mcp.datalicense.uk/mcp. Connect it from an MCP client such as Claude or Cursor.

dataset_licence({ "id": "deepghs/anime_blurry_preparation" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0