laion400m

• Description:

The LAION-400M dataset is completely openly, freely accessible.

Check https://laion.ai/laion-400-open-dataset/ for the full description of this dataset.

All images and texts in the LAION-400M dataset have been filtered with OpenAI‘s CLIP by calculating the cosine similarity between the text and image embeddings and dropping those with a similarity below 0.3. The threshold of 0.3 had been determined through human evaluations and seemed to be a good heuristic for estimating semantic image-text-content matching.

The image-text-pairs have been extracted from the Common Crawl web data dump and are from random web pages crawled between 2014 and 2021.

• Additional Documentation: Explore on Papers With Code north_east

• Homepage: https://laion.ai/blog/laion-400-open-dataset/

• Source code: tfds.vision_language.laion400m.Laion400m

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: Unknown size

• Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
  Refer to "Download Information" section on https://laion.ai/blog/laion-400-open-dataset/

• Auto-cached (documentation): Unknown

• Splits:

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe): Missing.

• Citation:

@article{DBLP:journals/corr/abs-2111-02114,
  author    = {Christoph Schuhmann and
               Richard Vencu and
               Romain Beaumont and
               Robert Kaczmarczyk and
               Clayton Mullis and
               Aarush Katta and
               Theo Coombes and
               Jenia Jitsev and
               Aran Komatsuzaki},
  title     = { {LAION-400M:} Open Dataset of CLIP-Filtered 400 Million Image-Text
               Pairs},
  journal   = {CoRR},
  volume    = {abs/2111.02114},
  year      = {2021},
  url       = {https://arxiv.org/abs/2111.02114},
  eprinttype = {arXiv},
  eprint    = {2111.02114},
  timestamp = {Fri, 05 Nov 2021 15:25:54 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-02114.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

laion400m/images (default config)

• Feature structure:

FeaturesDict({
    'caption': Text(shape=(), dtype=string),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'license': Text(shape=(), dtype=string),
    'nsfw': ClassLabel(shape=(), dtype=int64, num_classes=4),
    'original_height': Scalar(shape=(), dtype=int32),
    'original_width': Scalar(shape=(), dtype=int32),
    'similarity': Scalar(shape=(), dtype=float64),
    'url': Text(shape=(), dtype=string),
})

• Feature documentation:

laion400m/embeddings

• Feature structure:

FeaturesDict({
    'caption': Text(shape=(), dtype=string),
    'image_embedding': Tensor(shape=(512,), dtype=float16),
    'license': Text(shape=(), dtype=string),
    'nsfw': ClassLabel(shape=(), dtype=int64, num_classes=4),
    'original_height': Scalar(shape=(), dtype=int32),
    'original_width': Scalar(shape=(), dtype=int32),
    'similarity': Scalar(shape=(), dtype=float64),
    'text_embedding': Tensor(shape=(512,), dtype=float16),
    'url': Text(shape=(), dtype=string),
})

• Feature documentation: